new research on memory

Stanford researchers observe memory formation in real time

By Alan Toth

Why is it that someone who hasn’t ridden a bicycle in decades can likely jump on and ride away without a wobble, but could probably not recall more than a name or two from their 3rd grade class?

This may be because physical skills — dubbed motor memories by neuroscientists — are encoded differently in our brains than our memories for names or facts.

Now, a new study by scientists with the Wu Tsai Neurosciences Institute is revealing exactly how motor memories are formed and why they are so persistent. It may even help illuminate the root causes of movement disorders like Parkinson’s disease.

“We think motor memory is unique,” said Jun Ding , an associate professor of neurosurgery and of neurology. “Some studies on Alzheimer’s disease included participants who were previously musicians and couldn’t remember their own families, but they could still play beautiful music. Clearly, there’s a huge difference in the way that motor memories are formed.”

Memories are thought to be encoded in the brain in the pattern of activity in networks of hundreds or thousands of neurons, sometimes distributed across distant brain regions. The concept of such a memory trace — sometimes called a memory engram — has been around for more than a century, but identifying exactly what an engram is and how it is encoded has proven extremely challenging. Previous studies have shown that some forms of learning activate specific neurons, which reactivate when the learned memory is recalled. However, whether memory engram neurons exist for motor skill learning remains unknown.

Ding and postdoctoral scholars Richard Roth and Fuu-Jiun Hwang wanted to know how these engram-like groups of cells get involved in learning and remembering a new motor skill.

“When you’re first learning to shoot a basketball, you use a very diverse set of neurons each time you throw, but as you get better, you use a more refined set that’s the same every time,” said Roth. “These refined neuron pathways were thought to be the basis of a memory engram, but we wanted to know exactly how these pathways emerge.”

In their new study, published July 8, 2022 in Neuron , the researchers trained mice to use their paws to reach food pellets through a small slot. Using genetic wizardry developed by the lab of Liqun Luo , a Wu Tsai Neurosciences Institute colleague in the Department of Biology, the researchers were able to identify specific neurons in the brain’s motor cortex — an area responsible for controlling movements — that were activated during the learning process. The researchers tagged these potential engram cells with a fluorescent marker so they could see if they also played a role in recalling the memory later on.

When the researchers tested the animals’ memory of this new skill weeks later, they found that those mice that still remembered the skill showed increased activity in the same neurons that were first identified during the learning period, showing that these neurons were responsible for encoding the skill: the researchers had observed the formation of memory engrams.

But how do these particular groups of neurons take on responsibility for learning a new task in the first place? And how do they actually improve the animal’s performance?

To answer these questions, the researchers zoomed in closer. Using two-photon microscopy to observe these living circuits in action, they observed the so-called “engram neurons” reprogram themselves as the mice learned. Motor cortex engram cells took on new synaptic inputs — potentially reflecting information about the reaching movement — and themselves formed powerful new output connections in a distant brain region called the dorsolateral striatum — a key waystation through which the engram neurons can exert refined control over the animal’s movements. It was the first time anyone had observed the creation of new synaptic pathways on the same neuron population — both at the input and the output levels — in these two brain regions.

The ability to trace new memories forming in the mouse brain allowed the research team to weigh in on a long-standing debate about how skills are stored in the brain: are they controlled from one central memory trace, or engram, or is the memory redundantly stored across many different brain areas? Though this study cannot discount the idea of centralized memory, it does lend credibility to the opposing theory. Another fascinating question is whether the activation of these engram neurons is required for the performance of already learned motor tasks. The researchers speculated that by suppressing the activity of neurons that had been identified as part of the motor cortex memory engram, the mice probably still would be able to perform the task.

“Think of memory like a highway. If 101 and 280 are both closed, you could still get to Stanford from San Francisco, it would just take a lot longer,” said Ding.   

These findings suggest that, in addition to being dispersed, motor memories are highly redundant. The researchers say that as we repeat learned skills, we are continually reinforcing the motor engrams by building new connections — refining the skill. It’s what is meant by the term muscle memory — a refined, highly redundant network of motor engrams used so frequently that the associated skill seems automatic.

Ding believes that this constant repetition is one reason for the persistence of motor memory, but it’s not the only reason. Memory persistence may also be affected by a skill being associated with a reward, perhaps through the neurotransmitter dopamine. Though the research team did not directly address it in this study, Ding’s previous work in Parkinson’s disease suggests the connection.

“Current thinking is that Parkinson’s disease is the result of these motor engrams being blocked, but what if they’re actually being lost and people are forgetting these skills?” said Ding. “Remember that even walking is a motor skill that we all learned once, and it can potentially be forgotten.”

It’s a question that the researchers hope to answer in a follow-up study, because it may be the key to developing effective treatments for motor disorders. If Parkinson’s disease is the result of blocked motor memories, then patients should be able to improve their movement abilities by practicing and reinforcing these motor skills. On the other hand, if Parkinson’s destroys motor engrams and inhibits the creation of new ones — by targeting motor engram neurons and their synaptic connection observed in the team’s new study — then a completely different approach must be taken to deliver effective treatments.

“Our next goal is to understand what’s happening in movement disorders like Parkinson’s,” Ding said. “Obviously, we’re still a long way from a cure, but understanding how motor skills form is critical if we want to understand why they’re disrupted by disease.”

The research was published July 8 in Neuron: https://doi.org/10.1016/j.neuron.2022.06.006

Study authors were Fuu-Jiun Hwang, Richard H. Roth, Yu-Wei Wu, Yue Sun, Destany K. Kwon, Yu Liu, and Jun B. Ding.

The research was supported by the National Institutes of Health (NIH) and National Institute for Neurological Disease and Stroke (NINDS); the Klingenstein Foundation's Aligning Science Across Parkinson’s initiative; and GG gift fund, the Stanford School of Medicine Dean’s Postdoctoral Fellowship; and Parkinson’s Foundation Postdoctoral Fellowship.

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New Research on Memory From Psychological Science

• Episodic Memory
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• Long-Term Memory
• Psychological Science
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• Visual Memory

Read about the latest research on memory published in  Psychological Science,  a journal of the Association for Psychological Science.

Modifying Memory: Selectively Enhancing and Updating Personal Memories for a Museum Tour by Reactivating Them

Peggy L. St. Jacques and Daniel L. Schacter

Although researchers know that memories can be modified when they are retrieved, less is known about how the properties of reactivation affect memory. Researchers sent participants on a self-guided tour of a museum with a camera that automatically took pictures of their visit. Researchers used the pictures from the visit to reactivate participants’ memories of the tour either in the order they were experienced (reactivation-match) or out of order (reactivation-mismatch). Participants in the reactivation-match condition had better memory for the experienced images and greater false recognition of images that were not experienced, which suggests that manipulating the properties of reactivation can selectively influence memories by enhancing and distorting the memory via updating.

Visual Long-Term Memory Stores High-Fidelity Representations of Observed Actions

Zhisen Jiang Urgolites and Justin N. Wood     

Although we know humans store representations of actions in their long-term memory, the precision of these representations is not well understood. Participants completed a study phase in which they viewed images from different movement categories (jump, turn, kick). Researchers then showed participants two images from the same movement category (jump, jump) or different movement categories (jump, turn) and asked them to indicate which of the images they had seen in the study phase. Participants’ memory for the actions was similarly accurate regardless of whether the images were from the same or different movement categories, which indicates that visual long-term memory can store accurate detailed representations of observed actions.  

Attention Restores Discrete Items to Visual Short-Term Memory

Alexandra M. Murray, Anna C. Nobre, Ian A. Clark, André M. Cravo, and Mark G. Stokes

Can attention help restore forgotten items to visual short-term memory? Participants were shown randomly oriented arrows placed around a fixation cross. Researchers tested each participant’s memory for the location of one of the arrows. On half the trials, participants saw a cue indicating which arrow would be tested. The cue was placed at the location of the item (valid retro-cue) or around the central cross (neutral retro-cue). Memory accuracy was significantly higher on trials with the valid retro-cue. The authors suggest that selective attention during the maintenance of a memory can turn it from one that is relatively weak into one that is more robust, which allows for access to information that would otherwise be forgotten.

Dr.s St. Jacques & Schacter

At age 92, I would guess my memory is failing a bit, but I imagine it’s to be expected. However, I have discovered that I have total recall of the music and lyrics of 102 songs and an additional 62 for which I can remember substantial portions.

Having spent more than sixty years in the theatre, I have memorized quite a bit of material, all of which was lost shortly after the production. I have never thought of my memory as exceptional except for this recall of songs. While I have memorized several of my favorite poems by other poets, as a poet with a published book I have no recall of any of my own work except for one or two mini ones, and If anything, my autobiographical memory is subpar.

My academic history is sad indeed. I had to go an extra year in high school to graduate, having failed sophomore English not once, but twice. I simply would not, or could not, read Shakespeare. My IQ on immediately entering the army in 1943 was 109. After the war, I found that I could go to college at the government’s expense, but the Dean, seeing my high school transcripts, remarked, “You’ve got to be kidding.” I said, “No, you have to take me,” and his response was, “Yes, but we don’t have to keep you.” And they didn’t. At the end of another fateful sophomore year I became a college drop-out. BUT, following that, eight of the many years of a long career was spent teaching at Bethany College sans any degree, a career that culminated with the awarding of an Honorary Doctor of Humane Letters degree by the very same institution from which I dropped out some sixty years earlier. What any of this might have to do with my retention of both music and lyrics, I can’t imagine, but I most certainly would like to gain some insight.

Please let me know if you have any interest in this amazing recall.

Hal; You learned what meant something to you. You accessed that information that meant something to you in long term memory. When you could use that information, you taught it to others because you were motivated to and had the self-efficacy to do it. Chances are, you have a whole heck of lot of more information in there that you haven’t accessed because you might not have good feelings about it, from the sound of your recounted story. Remember, we branch to emotions, thoughts, and schemas within the brain when information goes to long term memory, and that may mean we have told ourselves that we can’t do it or don’t want to do it, whatever the task or extrinsic goal is. Remember, intrinsic goals and motivation beat extrinsic motivation every day of the week. This is what all the literature I have read says. I teach psychology and am an ABD PhD Candidate. Dissertation is in process of being reviewed by the university for approval. Barb Kaidy

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Scientists Discuss How to Study the Psychology of Collectives, Not Just Individuals

In a set of articles appearing in Perspectives on Psychological Science, an international array of scientists discusses how the study of neighborhoods, work units, activist groups, and other collectives can help us better understand and respond to societal changes.

Deconstructing Entrepreneurial Discovery

An adaption of a 2022 preprint article published in Technovation , this article explores how alertness might be related to entrepreneurial discovery and whether positivity or negativity are more associated with alertness.

Mix It Up: Testing Students on Unrelated Concepts Can Help Jump-Start Learning 

Unlike traditional “blocked” testing, which requires students to retrieve information about a single topic, interleaved testing presents a mix of topics from various lessons in order to encourage deeper conceptual learning.

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Monday, March 7, 2022

Researchers uncover how the human brain separates, stores, and retrieves memories

NIH-funded study identifies brain cells that form boundaries between discrete events.

Researchers have identified two types of cells in our brains that are involved in organizing discrete memories based on when they occurred. This finding improves our understanding of how the human brain forms memories and could have implications in memory disorders such as Alzheimer’s disease. The study was supported by the National Institutes of Health’s  Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and published in Nature Neuroscience .

“This work is transformative in how the researchers studied the way the human brain thinks,” said Jim Gnadt, Ph.D., program director at the National Institute of Neurological Disorders and Stroke and the NIH BRAIN Initiative. “It brings to human neuroscience an approach used previously in non-human primates and rodents by recording directly from neurons that are generating thoughts.”

This study, led by Ueli Rutishauser, Ph.D., professor of neurosurgery, neurology and biomedical sciences at Cedars-Sinai Medical Center in Los Angeles, started with a deceptively simple question: how does our brain form and organize memories? We live our awake lives as one continuous experience, but it is believed based on human behavior studies, that we store these life events as individual, distinct moments. What marks the beginning and end of a memory? This theory is referred to as “event segmentation,” and we know relatively little about how the process works in the human brain.

To study this, Rutishauser and his colleagues worked with 20 patients who were undergoing intracranial recording of brain activity to guide surgery for treatment of their drug-resistant epilepsy. They looked at how the patients’ brain activity was affected when shown film clips containing different types of “cognitive boundaries”—transitions thought to trigger changes in how a memory is stored and that mark the beginning and end of memory “files” in the brain.

The first type, referred to as a “soft boundary,” is a video containing a scene that then cuts to another scene that continues the same story. For example, a baseball game showing a pitch is thrown and, when the batter hits the ball, the camera cuts to a shot of the fielder making a play. In contrast, a “hard boundary” is a cut to a completely different story—imagine if the batted ball were immediately followed by a cut to a commercial.

Jie Zheng, Ph.D., postdoctoral fellow at Children’s Hospital Boston and first author of the study, explained the key difference between the two boundaries.

“Is this a new scene within the same story, or are we watching a completely different story? How much the narrative changes from one clip to the next determines the type of cognitive boundary,” said Zheng.  

The researchers recorded the brain activity of participants as they watched the videos, and they noticed two distinct groups of cells that responded to different types of boundaries by increasing their activity. One group, called “boundary cells” became more active in response to either a soft or hard boundary. A second group, referred to as “event cells” responded only to hard boundaries. This led to the theory that the creation of a new memory occurs when there is a peak in the activity of both boundary and event cells, which is something that only occurs following a hard boundary.

One analogy to how memories might be stored and accessed in the brain is how photos are stored on your phone or computer. Often, photos are automatically grouped into events based on when and where they were taken and then later displayed to you as a key photo from that event. When you tap or click on that photo, you can drill down into that specific event.

“A boundary response can be thought of like creating a new photo event,” said Dr. Rutishauser. “As you build the memory, it’s like new photos are being added to that event. When a hard boundary occurs, that event is closed and a new one begins. Soft boundaries can be thought of to represent new images created within a single event.” 

The researchers next looked at memory retrieval and how this process relates to the firing of boundary and event cells. They theorized that the brain uses boundary peaks as markers for “skimming” over past memories, much in the way the key photos are used to identify events. When the brain finds a firing pattern that looks familiar, it “opens” that event.

Two different memory tests designed to study this theory were used. In the first, the participants were shown a series of still images and were asked whether they were from a scene in the film clips they just watched. Study participants were more likely to remember images that occurred soon after a hard or soft boundary, which is when a new “photo” or “event” would have been created.

The second test involved showing pairs of images taken from film clips that they had just watched. The participants were then asked which of the two images had appeared first. It turned out that they had a much harder time choosing the correct image if the two occurred on different sides of a hard boundary, possibly because they had been placed in different “events.”

These findings provide a look into how the human brain creates, stores, and accesses memories. Because event segmentation is a process that can be affected in people living with memory disorders, these insights could be applied to the development of new therapies.

In the future, Dr. Rutishauser and his team plan to look at two possible avenues to develop therapies related to these findings. First, neurons that use the chemical dopamine, which are most-known for their role in reward mechanisms, may be activated by boundary and event cells, suggesting a possible target to help strengthen the formation of memories.

Second, one of the brain’s normal internal rhythms, known as the theta rhythm, has been connected to learning and memory. If event cells fired in time with that rhythm, the participants had an easier time remembering the order of the images that they were shown. Because deep brain stimulation can affect theta rhythms, this could be another avenue for treating patients with certain memory disorders.

This project was made possible by a multi-institutional consortium through the NIH BRAIN Initiative’s Research on Humans program. Institutions involved in this study were Cedars-Sinai Medical Center, Children’s Hospital Boston (site PI Gabriel Kreiman, Ph.D.), and Toronto Western Hospital (site PI Taufik Valiante, M.D., Ph.D.). The study was funded by the NIH BRAIN Initiative (NS103792, NS117839), the National Science Foundation, and Brain Canada.

The BRAIN Initiative ® is a registered trademark of the U.S. Department of Health and Human Services.

The NIH BRAIN Initiative   is managed by 10 institutes whose missions and current research portfolios complement the goals of The BRAIN Initiative ® : National Center for Complementary and Integrative Health, National Eye Institute, National Institute on Aging, National Institute on Alcohol Abuse and Alcoholism, National Institute of Biomedical Imaging and Bioengineering,  Eunice Kennedy Shriver  National Institute of Child Health and Human Development, National Institute on Drug Abuse, National Institute on Deafness and other Communication Disorders, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke.

NINDS  ( https://www.ninds.nih.gov ) is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Zheng J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience. March 7, 2022. DOI: 10.1038/s41593-022-01020-w

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Study sheds light on how neurons form long-term memories

On a late summer day in 1953, a young man who would soon be known as patient H.M. underwent experimental surgery. In an attempt to treat his debilitating seizures, a surgeon removed portions of his brain, including part of a structure called the hippocampus. The seizures stopped.

Unfortunately, for patient H.M., so too did time. When he woke up after surgery, he could no longer form new long-term memories, despite retaining normal cognitive abilities, language and short-term working memory. Patient H.M.’s condition ultimately revealed that the brain’s ability to create long-term memories is a distinct process that depends on the hippocampus.

Scientists had discovered where memories are made. But  how  they are made remained unknown.

Now, neuroscientists at Harvard Medical School (HMS) have taken a decisive step in the quest to understand the biology of long-term memory and find ways to intervene when memory deficits occur with age or disease.

Reporting in  Nature  on Dec. 9, they describe a newly identified mechanism that neurons in the adult mouse hippocampus use to regulate signals they receive from other neurons, in a process that appears critical for memory consolidation and recall.

The study was led by  Lynn Yap , HMS graduate student in neurobiology, and  Michael Greenberg , chair of neurobiology in the Blavatnik Institute at HMS.

“If we can better understand this process, we will have new handles on memory and how to intervene when things go wrong, …” Michael Greenberg, Blavatnik Institute at HMS

“Memory is essential to all aspects of human existence. The question of how we encode memories that last a lifetime is a fundamental one, and our study gets to the very heart of this phenomenon,” said Greenberg, the HMS Nathan Marsh Pusey Professor of Neurobiology and study corresponding author.

The researchers observed that new experiences activate sparse populations of neurons in the hippocampus that express two genes, Fos and Scg2. These genes allow neurons to fine-tune inputs from so-called inhibitory interneurons, cells that dampen neuronal excitation. In this way, small groups of disparate neurons may form persistent networks with coordinated activity in response to an experience.

“This mechanism likely allows neurons to better talk to each other so that the next time a memory needs to be recalled, the neurons fire more synchronously,” Yap said. “We think coincident activation of this Fos-mediated circuit is potentially a necessary feature for memory consolidation, for example, during sleep, and also memory recall in the brain.”

Circuit orchestration

In order to form memories, the brain must somehow wire an experience into neurons so that when these neurons are reactivated, the initial experience can be recalled. In their study, Greenberg, Yap and team set out to explore this process by looking at the gene Fos.

First  described  in neuronal cells by Greenberg and colleagues in 1986, Fos is expressed within minutes after a neuron is activated. Scientists have taken advantage of this property, using Fos as a marker of recent neuronal activity to identify brain cells that regulate thirst,  torpor , and many other behaviors.

Scientists hypothesized that Fos might play a critical role in learning and memory, but for decades, the precise function of the gene has remained a mystery.

To investigate, the researchers exposed mice to new environments and looked at pyramidal neurons, the principal cells of the hippocampus. They found that relatively sparse populations of neurons expressed Fos after exposure to a new experience. Next, they prevented these neurons from expressing Fos, using a virus-based tool delivered to a specific area of the hippocampus, which left other cells unaffected.

Mice that had Fos blocked in this manner showed significant memory deficits when assessed in a maze that required them to recall spatial details, indicating that the gene plays a critical role in memory formation.

The researchers studied the differences between neurons that expressed Fos and those that did not. Using  optogenetics  to turn inputs from different nearby neurons on or off, they discovered that the activity of Fos-expressing neurons was most strongly affected by two types of interneurons.

Neurons expressing Fos were found to receive increased activity-dampening, or inhibitory, signals from one distinct type of interneuron and decreased inhibitory signals from another type. These signaling patterns disappeared in neurons with blocked Fos expression.

“What’s critical about these interneurons is that they can regulate when and how much individual Fos-activated neurons fire, and also when they fire relative to other neurons in the circuit,” Yap said. “We think that at long last we have a handle on how Fos may in fact support memory processes, specifically by orchestrating this type of circuit plasticity in the hippocampus.”

Imagine the day

The researchers further probed the function of Fos, which codes for a transcription factor protein that regulates other genes. They used single-cell sequencing and additional genomic screens to identify genes activated by Fos and found that one gene in particular, Scg2, played a critical role in regulating inhibitory signals.

In mice with experimentally silenced Scg2, Fos-activated neurons in the hippocampus displayed a defect in signaling from both types of interneurons. These mice also had defects in theta and gamma rhythms, brain properties thought to be critical features of learning and memory.

Previous studies had shown that Scg2 codes for a neuropeptide protein that can be cleaved into four distinct forms, which are then secreted. In the current study, Yap and colleagues discovered that neurons appear to use these neuropeptides to fine-tune inputs they receive from interneurons.

Together, the team’s experiments suggest that after a new experience, a small group of neurons simultaneously express Fos, activating Scg2 and its derived neuropeptides, in order to establish a coordinated network with its activity regulated by interneurons.

“When neurons are activated in the hippocampus after a new experience, they aren’t necessarily linked together in any particular way in advance,” Greenberg said. “But interneurons have very broad axonal arbors, meaning they can connect with and signal to many cells at once. This may be how a sparse group of neurons can be linked together to ultimately encode a memory.”

The study findings represent a possible molecular- and circuit-level mechanism for long-term memory. They shed new light on the fundamental biology of memory formation and have broad implications for diseases of memory dysfunction.

The researchers note, however, that while the results are an important step in our understanding of the inner workings of memory, numerous unanswered questions about the newly identified mechanisms remain.

“We’re not quite at the answer yet, but we can now see many of the next steps that need to be taken,” Greenberg said. “If we can better understand this process, we will have new handles on memory and how to intervene when things go wrong, whether in age-related memory loss or neurodegenerative disorders such as Alzheimer’s disease.”

The findings also represent the culmination of decades of research, even as they open new avenues of study that will likely take decades more to explore, Greenberg added.

“I arrived at Harvard in 1986, just as my paper describing the discovery that neuronal activity can turn on genes was published,” he said. “Since that time, I’ve been imagining the day when we would figure out how genes like Fos might contribute to long-term memory.”

Additional authors include Noah Pettit, Christopher Davis, M. Aurel Nagy, David Harmin, Emily Golden, Onur Dagliyan, Cindy Lin, Stephanie Rudolph, Nikhil Sharma, Eric Griffith, and Christopher Harvey.

The study was supported by the National Institutes of Health (grants R01NS028829, R01NS115965, R01NS089521, T32NS007473 and F32NS112455), a Stuart H.Q. and Victoria Quan fellowship, a Harvard Department of Neurobiology graduate fellowship, an Aramont Fund.

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Cognitive neuroscience perspective on memory: overview and summary

Sruthi sridhar.

1 Department of Psychology, Mount Allison University, Sackville, NB, Canada

Abdulrahman Khamaj

2 Department of Industrial Engineering, College of Engineering, Jazan University, Jazan, Saudi Arabia

Manish Kumar Asthana

3 Department of Humanities and Social Sciences, Indian Institute of Technology Roorkee, Roorkee, India

4 Department of Design, Indian Institute of Technology Roorkee, Roorkee, India

Associated Data

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampal-neocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory.

Introduction

Memory is an essential cognitive function that permits individuals to acquire, retain, and recover data that defines a person’s identity ( Zlotnik and Vansintjan, 2019 ). Memory is a multifaceted cognitive process that involves different stages: encoding, consolidation, recovery, and reconsolidation. Encoding involves acquiring and processing information that is transformed into a neuronal representation suitable for storage ( Liu et al., 2021 ; Panzeri et al., 2023 ). The information can be acquired through various channels, such as visual, auditory, olfactory, or tactile inputs. The acquired sensory stimuli are converted into a format the brain can process and retain. Different factors such as attention, emotional significance, and repetition can influence the encoding process and determine the strength and durability of the resulting memory ( Squire et al., 2004 ; Lee et al., 2016 ; Serences, 2016 ).

Consolidation includes the stabilization and integration of memory into long-term storage to increase resistance to interference and decay ( Goedert and Willingham, 2002 ). This process creates enduring structural modification in the brain and thereby has consequential effects on the function by reorganizing and strengthening neural connections. Diverse sources like sleep and stress and the release of neurotransmitters can influence memory consolidation. Many researchers have noted the importance of sleep due to its critical role in enabling a smooth transition of information from transient repositories into more stable engrams (memory traces) ( McGaugh, 2000 ; Clawson et al., 2021 ; Rakowska et al., 2022 ).

Retrieval involves accessing, selecting, and reactivating or reconstructing the stored memory to allow conscious access to previously encoded information ( Dudai, 2002 ). Retrieving memories depends on activating relevant neural pathways while reconstructing encoded information. Factors like contextual or retrieval cues and familiarity with the material can affect this process. Forgetting becomes a possibility if there are inadequate triggers for associated memory traces to activate upon recall. Luckily, mnemonic strategies and retrieval practice offer effective tools to enhance recovery rates and benefit overall memory performance ( Roediger and Butler, 2011 ).

Previous research implied that once a memory has been consolidated, it becomes permanent ( McGaugh, 2000 ; Robins, 2020 ). However, recent studies have found an additional phase called “reconsolidation,” during which stored memories, when reactivated, enter a fragile or liable state and become susceptible to modification or update ( Schiller et al., 2009 ; Asthana et al., 2015 ). The process highlights the notion that memory is not static but a dynamic system influenced by subsequent encounters. The concept of reconsolidation has much significance in memory modification therapies and interventions, as it offers a promising opportunity to target maladaptive or traumatic memories for modification specifically. However, more thorough investigations are needed to gain insight into the mechanisms and concrete implications of employing memory reconsolidation within therapeutic settings ( Bellfy and Kwapis, 2020 ).

The concept of memory is not reducible to a single unitary phenomenon; instead, evidence suggests that it can be subdivided into several distinct but interrelated constituent processes and systems ( Richter-Levin and Akirav, 2003 ). There are three major types of human memory: working memory, declarative memory (explicit), and non-declarative memory (implicit). All these types of memories involve different neural systems in the brain. Working memory is a unique transient active store capable of manipulating information essential for many complex cognitive operations, including language processing, reasoning, and judgment ( Atkinson and Shiffrin, 1968 ; Baddeley and Logie, 1999 ; Funahashi, 2017 ; Quentin et al., 2019 ). Previous models suggest the existence of three components that make up the working memory ( Baddeley and Hitch, 1974 ; Baddeley, 1986 ). One master component, the central executive, controls the two dependent components, the phonological loop (speech perception and language comprehension) and the visuospatial sketchpad (visual images and spatial impressions processing). Some models mention a third component known as the episodic buffer. It is theorized that the episodic buffer serves as an intermediary between perception, long-term memory, and two components of working memory (the phonological loop and visuospatial sketchpad) by storing integrated episodes or chunks from both sources ( Baddeley, 2000 ). Declarative memory (explicit memory) can be recalled consciously, including facts and events that took place in one’s life or information learned from books. It encompasses memories of both autobiographical experiences and memories associated with general knowledge. It is usually associated with the hippocampus–medial temporal lobe system ( Thompson and Kim, 1996 ; Ober, 2014 ). Non-declarative memory (implicit memory) refers to unconscious forms of learning such as skills, habits, and priming effects; this type of implicit learning does not involve conscious recollection but can include motor skill tasks that often require no thought prior to execution nor later recall upon completion. This type of memory usually involves the amygdala and other systems ( Thompson and Kim, 1996 ; Ober, 2014 ).

Working memory

Working memory is primarily associated with the prefrontal and posterior parietal cortex ( Sarnthein et al., 1998 ; Todd and Marois, 2005 ). Working memory is not localized to a single brain region, and research suggests that it is an emergent property arising from functional interactions between the prefrontal cortex (PFC) and the rest of the brain ( D’Esposito, 2007 ). Neuroimaging studies have explored the neural basis for the three components proposed by Baddeley and Hitch (1974) , the Central executive, the phonological loop, and the visuospatial sketch pad; there is evidence for the existence of a fourth component called the episodic buffer ( Baddeley, 2000 ).

The central executive plays a significant role in working memory by acting as the control center ( Shallice, 2002 ). It facilitates critical functions like attention allocation and coordination between the phonological loop and the visuospatial sketchpad ( Yu et al., 2023 ). Recent findings have illuminated the dual-functional network regulation, the cingulo-opercular network (CON) and the frontoparietal network (FPN), that underpins the central executive system ( Yu et al., 2023 ). The CON comprises the dorsal anterior cingulate cortex (dACC) and anterior insula (AI). In contrast, the FPN encompasses various regions, such as the dorsolateral prefrontal cortex (DLPFC) and frontal eye field (FEF), along with the intraparietal sulcus (IPS) ( Yu et al., 2023 ). Neuroimaging research has found evidence that elucidates the neural underpinnings of the executive attention control system to the dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) ( Jung et al., 2022 ). The activation patterns indicate that the CON may have a broader top-down control function across the working memory process. At the same time, the FPN could be more heavily implicated in momentary control or processing at the trial level ( Yu et al., 2023 ). Evidence suggests that the central executive interacts with the phonological loop and visuospatial sketchpad to support working memory processes ( Baddeley, 2003 ; Buchsbaum, 2010 ; Menon and D’Esposito, 2021 ). The function, localization, and neural basis of this interaction are thought to involve the activation of specific brain regions associated with each component of working memory, as discussed in detail below.

The phonological loop is divided into two components: a storage system that maintains information (a few seconds) and a component involving subvocal rehearsal—which maintains and refreshes information in the working memory. Neuroanatomically, the phonological loop is represented in the Brodmann area (BA) 40 in the parietal cortex and the rehearsal components in BA 44 and 6, both situated in the frontal cortex ( Osaka et al., 2007 ). The left inferior frontal gyrus (Broca’s area) and the left posterior superior temporal gyrus (Wernicke’s area) has been proposed to play a critical role in supporting phonological and verbal working memory tasks, specifically the subvocal rehearsal system of the articulatory loop ( Paulesu et al., 1993 ; Buchsbaum et al., 2001 ; Perrachione et al., 2017 ). The phonological store in verbal short-term memory has been localized at the left supramarginal gyrus ( Graves et al., 2008 ; Perrachione et al., 2017 ).

Studies utilizing neuroimaging techniques have consistently yielded results indicating notable activation in these brain regions during phonological activities like recalling non-words and maintaining verbal information in memory ( Awh et al., 1996 ; Graves et al., 2008 ). During tasks that require phonological rehearsal, there was an increase in activation in the left inferior frontal gyrus ( Paulesu et al., 1993 ). Researchers have noted an increase in activity within the superior temporal gyrus-which plays a significant role in auditory processing-in individuals performing tasks that necessitate verbal information maintenance and manipulation ( Smith et al., 1998 ; Chein et al., 2003 ).

Additionally, lesion studies have provided further confirmation regarding the importance of these regions. These investigations have revealed that impairment in performing phonological working memory tasks can transpire following damage inflicted upon the left hemisphere, particularly on perisylvian language areas ( Koenigs et al., 2011 ). It is common for individuals with lesions affecting regions associated with the phonological loop, such as the left inferior frontal gyrus and superior temporal gyrus, to have difficulty performing verbal working memory tasks. Clinical cases involving patients diagnosed with aphasia and specific language impairments have highlighted challenges related to retaining and manipulating auditory information. For example, those who sustain damage specifically within their left inferior frontal gyrus often struggle with tasks involving phonological rehearsal and verbal working memory activities, and therefore, they tend to perform poorly in tasks that require manipulation or repetition of verbal stimuli ( Saffran, 1997 ; Caplan and Waters, 2005 ).

The visuospatial sketchpad is engaged in the temporary retention and manipulation of visuospatial facts, including mental pictures, spatial associations, and object placements ( Miyake et al., 2001 ). The visuospatial sketchpad is localized to the right hemisphere, including the occipital lobe, parietal and frontal areas ( Osaka et al., 2007 ). Ren et al. (2019) identified the localization of the visuospatial sketchpad, and these areas were the right infero-lateral prefrontal cortex, lateral pre-motor cortices, right inferior parietal cortex, and the dorsolateral occipital cortices ( Burbaud et al., 1999 ; Salvato et al., 2021 ). Moreover, the posterior parietal cortex and the intraparietal sulcus have been implicated in spatial working memory ( Xu and Chun, 2006 ). Additionally, some evidence is available for an increase in brain regions associated with the visuospatial sketchpad during tasks involving mental imagery and spatial processing. Neuroimaging studies have revealed increased neural activation in some regions of the parietal cortex, mainly the superior and posterior parietal cortex, while performing mental rotation tasks ( Cohen et al., 1996 ; Kosslyn et al., 1997 ). However, further research is needed to better understand the visuospatial working memory and its integration with other cognitive processes ( Baddeley, 2003 ). Lesions to the regions involving the visuospatial sketchpad can have detrimental effects on visuospatial working memory tasks. Individuals with lesions to the posterior parietal cortex may exhibit deficits in mental rotation tasks and may be unable to mentally manipulate the visuospatial representation ( Buiatti et al., 2011 ). Moreover, studies concerning lesions have shown that damage to the parietal cortex can result in short-term deficits in visuospatial memory ( Shafritz et al., 2002 ). Damage to the occipital cortex can lead to performance impairments in tasks that require the generation and manipulation of mental visual images ( Moro et al., 2008 ).

The fourth component of the working memory, termed episodic buffer, was proposed by Baddeley (2000) . The episodic buffer is a multidimensional but essentially passive store that can hold a limited number of chunks, store bound features, and make them available to conscious awareness ( Baddeley et al., 2010 ; Hitch et al., 2019 ). Although research has suggested that episodic buffer is localized to the hippocampus ( Berlingeri et al., 2008 ) or the inferior lateral parietal cortex, it is thought to be not dependent on a single anatomical structure but instead can be influenced by the subsystems of working memory, long term memory, and even through perception ( Vilberg and Rugg, 2008 ; Baddeley et al., 2010 ). The episodic buffer provides a crucial link between the attentional central executive and the multidimensional information necessary for the operation of working memory ( Baddeley et al., 2011 ; Gelastopoulos et al., 2019 ).

The interdependence of the working memory modules, namely the phonological loop and visuospatial sketchpad, co-relates with other cognitive processes, for instance, spatial cognition and attention allocation ( Repovs and Baddeley, 2006 ). It has been found that the prefrontal cortex (PFC) and posterior parietal cortex (PPC) have a crucial role in several aspects of spatial cognition, such as the maintenance of spatially oriented attention and motor intentions ( Jerde and Curtis, 2013 ). The study by Sellers et al. (2016) and the review by Ikkai and Curtis (2011) posits that other brain areas could use the activity in PFC and PPC as a guide and manifest outputs to guide attention allocation, spatial memory, and motor planning. Moreover, research indicates that verbal information elicits an activation response in the left ventrolateral prefrontal cortex (VLPFC) when retained in the phonological loop, while visuospatial information is represented by a corresponding level of activity within the right homolog region ( Narayanan et al., 2005 ; Wolf et al., 2006 ; Emch et al., 2019 ). Specifically, the study by Yang et al. (2022) investigated the roles of two regions in the brain, the right inferior frontal gyrus (rIFG) and the right supra-marginal gyrus (rSMG), as they relate to spatial congruency in visual working memory tasks. A change detection task with online repetitive transcranial magnetic stimulation applied concurrently at both locations during high visual WM load conditions determined that rIFG is involved in actively repositioning the location of objects. At the same time, rSMG is engaged in passive perception of the stability of the location of objects.

Recent academic studies have found evidence to support the development of a new working memory model known as the state-based model ( D’Esposito and Postle, 2015 ). This theoretical model proposes that the allocation of attention toward internal representations permits short-term retention within working memory ( Ghaleh et al., 2019 ). The state-based model consists of two main categories: activated LTM models and sensorimotor recruitment models; the former largely focuses upon symbolic stimuli categorized under semantic aspects, while the latter has typically been applied to more perceptual tasks in experiments. This framework posits that prioritization through regulating cognitive processes provides insight into various characteristics across different activity types, including capacity limitations, proactive interference, etcetera ( D’Esposito and Postle, 2015 ). For example, the paper by Ghaleh et al. (2019) provides evidence for two separate mechanisms involved in maintenance of auditory information in verbal working memory: an articulatory rehearsal mechanism that relies more heavily on left sensorimotor areas and a non-articulatory maintenance mechanism that critically relies on left superior temporal gyrus (STG). These findings support the state-based model’s proposal that attentional allocation is necessary for short-term retention in working memory.

State-based models were found to be consistent with the suggested storage mechanism as they do not require representation transfer from one dedicated buffer type; research has demonstrated that any population of neurons and synapses may serve as such buffers ( Maass and Markram, 2002 ; Postle, 2006 ; Avraham et al., 2017 ). The review by D’Esposito and Postle (2015) examined the evidence to determine whether a persistent neural activity, synaptic mechanisms, or a combination thereof support representations maintained during working memory. Numerous neural mechanisms have been hypothesized to support the short-term retention of information in working memory and likely operate in parallel ( Sreenivasan et al., 2014 ; Kamiński and Rutishauser, 2019 ).

Persistent neural activity is the neural mechanism by which information is temporarily maintained ( Ikkai and Curtis, 2011 ; Panzeri et al., 2023 ). Recent review by Curtis and Sprague (2021) has focused on the notion that persistent neural activity is a fundamental mechanism for memory storage and have provided two main arcs of explanation. The first arc, mainly underpinned by empirical evidence from prefrontal cortex (PFC) neurophysiology experiments and computational models, posits that PFC neurons exhibit sustained firing during working memory tasks, enabling them to store representations in their active state ( Thuault et al., 2013 ). Intrinsic persistent firing in layer V neurons in the medial PFC has been shown to be regulated by HCN1 channels, which contribute to the executive function of the PFC during working memory episodes ( Thuault et al., 2013 ). Additionally, research has also found that persistent neural firing could possibly interact with theta periodic activity to sustain each other in the medial temporal, prefrontal, and parietal regions ( Düzel et al., 2010 ; Boran et al., 2019 ). The second arc involves advanced neuroimaging approaches which have, more recently, enabled researchers to decode content stored within working memories across distributed regions of the brain, including parts of the early visual cortex–thus extending this framework beyond just isolated cortical areas such as the PFC. There is evidence that suggests simple, stable, persistent activity among neurons in stimulus-selective populations may be a crucial mechanism for sustaining WM representations ( Mackey et al., 2016 ; Kamiński et al., 2017 ; Curtis and Sprague, 2021 ).

Badre (2008) discussed the functional organization of the PFC. The paper hypothesized that the rostro-caudal gradient of a function in PFC supported a control hierarchy, whereas posterior to anterior PFC mediated progressively abstract, higher-order controls ( Badre, 2008 ). However, this outlook proposed by Badre (2008) became outdated; the paper by Badre and Nee (2018) presented an updated look at the literature on hierarchical control. This paper supports neither a unitary model of lateral frontal function nor a unidimensional abstraction gradient. Instead, separate frontal networks interact via local and global hierarchical structures to support diverse task demands. This updated perspective is supported by recent studies on the hierarchical organization of representations within the lateral prefrontal cortex (LPFC) and the progressively rostral areas of the LPFC that process/represent increasingly abstract information, facilitating efficient and flexible cognition ( Thomas Yeo et al., 2011 ; Nee and D’Esposito, 2016 ). This structure allows the brain to access increasingly abstract action representations as required ( Nee and D’Esposito, 2016 ). It is supported by fMRI studies showing an anterior-to-posterior activation movement when tasks become more complex. Anatomical connectivity between areas also supports this theory, such as Area 10, which has projections back down to Area 6 but not vice versa.

Finally, studies confirm that different regions serve different roles along a hierarchy leading toward goal-directed behavior ( Badre and Nee, 2018 ). The paper by Postle (2015) exhibits evidence of activity in the prefrontal cortex that reflects the maintenance of high-level representations, which act as top-down signals, and steer the circulation of neural pathways across brain networks. The PFC is a source of top-down signals that influence processing in the posterior and subcortical regions ( Braver et al., 2008 ; Friedman and Robbins, 2022 ). These signals either enhance task-relevant information or suppress irrelevant stimuli, allowing for efficient yet effective search ( D’Esposito, 2007 ; D’Esposito and Postle, 2015 ; Kerzel and Burra, 2020 ). The study by Ratcliffe et al. (2022) provides evidence of the dynamic interplay between executive control mechanisms in the frontal cortex and stimulus representations held in posterior regions for working memory tasks. Moreover, the review by Herry and Johansen (2014) discusses the neural mechanisms behind actively maintaining task-relevant information in order for a person to carry out tasks and goals effectively. This review of data and research suggests that working memory is a multi-component system allowing for both the storage and processing of temporarily active representations. Neural activity throughout the brain can be differentially enhanced or suppressed based on context through top-down signals emanating from integrative areas such as PFC, parietal cortex, or hippocampus to actively maintain task-relevant information when it is not present in the environment ( Herry and Johansen, 2014 ; Kerzel and Burra, 2020 ).

In addition, Yu et al. (2022) examined how brain regions from the ventral stream pathway to the prefrontal cortex were activated during working memory (WM) gate opening and closing. They defined gate opening as the switch from maintenance to updating and gate closing as the switch from updating to maintenance. The data suggested that cognitive branching increases during the WM gating process, thus correlating the gating process and an information approach to the PFC function. The temporal cortices, lingual gyrus (BA19), superior frontal gyri including frontopolar cortices, and middle and inferior parietal regions are involved in processes of estimating whether a response option available will be helpful for each case. During gate closing, on the other hand, medial and superior frontal regions, which have been associated with conflict monitoring, come into play, as well as orbitofrontal and dorsolateral prefrontal processing at later times when decreasing activity resembling stopping or downregulating cognitive branching has occurred, confirming earlier theories about these areas being essential for estimation of usefulness already stored within long-term memories ( Yu et al., 2022 ).

Declarative and non-declarative memory

The distinctions between declarative and non-declarative memory are often based on the anatomical features of medial temporal lobe regions, specifically those involving the hippocampus ( Squire and Zola, 1996 ; Squire and Wixted, 2011 ). In the investigation of systems implicated in the process of learning and memory formation, it has been posited that the participation of the hippocampus is essential for the acquisition of declarative memories ( Eichenbaum and Cohen, 2014 ). In contrast, a comparatively reduced level of hippocampal involvement may suffice for non-declarative memories ( Squire and Zola, 1996 ; Williams, 2020 ).

Declarative memory (explicit) pertains to knowledge about facts and events. This type of information can be consciously retrieved with effort or spontaneously recollected without conscious intention ( Dew and Cabeza, 2011 ). There are two types of declarative memory: Episodic and Semantic. Episodic memory is associated with the recollection of personal experiences. It involves detailed information about events that happened in one’s life. Semantic memory refers to knowledge stored in the brain as facts, concepts, ideas, and objects; this includes language-related information like meanings of words and mathematical symbol values along with general world knowledge (e.g., capitals of countries) ( Binder and Desai, 2011 ). The difference between episodic and semantic memory is that when one retrieves episodic memory, the experience is known as “remembering”; when one retrieves information from semantic memory, the experience is known as “knowing” ( Tulving, 1985 ; Dew and Cabeza, 2011 ). The hippocampus, medial temporal lobe, and the areas in the diencephalon are implicated in declarative memory ( Richter-Levin and Akirav, 2003 ; Derner et al., 2020 ). The ventral parietal cortex (VPC) is involved in declarative memory processes, specifically episodic memory retrieval ( Henson et al., 1999 ; Davis et al., 2018 ). The evidence suggests that VPC and hippocampus is involved in the retrieval of contextual details, such as the location and timing of the event, and the information is critical for the formation of episodic memory ( Daselaar, 2009 ; Hutchinson et al., 2009 ; Wiltgen et al., 2010 ). The prefrontal cortex (PFC) is involved in the encoding (medial PFC) and retrieval (lateral PFC) of declarative memories, specifically in the integration of information across different sensory modalities ( Blumenfeld and Ranganath, 2007 ; Li et al., 2010 ). Research also suggests that the amygdala may modulate other brain regions involved with memory processing, thus, contributing to an enhanced recall of negative or positive experiences ( Hamann, 2001 ; Ritchey et al., 2008 ; Sendi et al., 2020 ). Maintenance of the integrity of hippocampal circuitry is essential for ensuring that episodic memory, along with spatial and temporal context information, can be retained in short-term or long-term working memory beyond 15 min ( Ito et al., 2003 ; Rasch and Born, 2013 ). Moreover, studies have suggested that the amygdala plays a vital role in encoding and retrieving explicit memories, particularly those related to emotionally charged stimuli which are supported by evidence of correlations between hippocampal activity and amygdala modulation during memory formation ( Richter-Levin and Akirav, 2003 ; Qasim et al., 2023 ).

Current findings in neuroimaging studies assert that a vast array of interconnected brain regions support semantic memory ( Binder and Desai, 2011 ). This network merges information sourced from multiple senses alongside different cognitive faculties necessary for generating abstract supramodal views on various topics stored within our consciousness. Modality-specific sensory, motor, and emotional system within these brain regions serve specialized tasks like language comprehension, while larger areas of the brain, such as the inferior parietal lobe and most of the temporal lobe, participate in more generalized interpretation tasks ( Binder and Desai, 2011 ; Kuhnke et al., 2020 ). These regions lie at convergences of multiple perceptual processing streams, enabling increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions, including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future ( Binder and Desai, 2011 ; Binney et al., 2016 ). The following section will discuss the processes underlying memory consolidation and storage within declarative memory.

Non-declarative (implicit) memories refer to unconscious learning through experience, such as habits and skills formed from practice rather than memorizing facts; these are typically acquired slowly and automatically in response to sensory input associated with reward structures or prior exposure within our daily lives ( Kesner, 2017 ). Non-declarative memory is a collection of different phenomena with different neural substrates rather than a single coherent system ( Camina and Güell, 2017 ). It operates by similar principles, depending on local changes to a circumscribed brain region, and the representation of these changes is unavailable to awareness ( Reber, 2008 ). Non-declarative memory encompasses a heterogenous collection of abilities, such as associative learning, skills, and habits (procedural memory), priming, and non-associative learning ( Squire and Zola, 1996 ; Camina and Güell, 2017 ). Studies have concluded that procedural memory for motor skills depends upon activity in diverse set areas such as the motor cortex, striatum, limbic system, and cerebellum; similarly, perceptual skill learning is thought to be associated with sensory cortical activation ( Karni et al., 1998 ; Mayes, 2002 ). Research suggests that mutual connections between brain regions that are active together recruit special cells called associative memory cells ( Wang et al., 2016 ; Wang and Cui, 2018 ). These cells help integrate, store, and remember related information. When activated, these cells trigger the recall of memories, leading to behaviors and emotional responses. This suggests that co-activated brain regions with these mutual connections are where associative memories are formed ( Wang et al., 2016 ; Wang and Cui, 2018 ). Additionally, observational data reveals that priming mechanisms within distinct networks, such as the “repetition suppression” effect observed in visual cortical areas associated with sensory processing and in the prefrontal cortex for semantic priming, are believed to be responsible for certain forms of conditioning and implicit knowledge transfer experiences exhibited by individuals throughout their daily lives ( Reber, 2008 ; Wig et al., 2009 ; Camina and Güell, 2017 ). However, further research is needed to better understand the mechanisms of consolidation in non-declarative memory ( Camina and Güell, 2017 ).

The process of transforming memory into stable, long-lasting from a temporary, labile memory is known as memory consolidation ( McGaugh, 2000 ). Memory formation is based on the change in synaptic connections of neurons representing the memory. Encoding causes synaptic Long-Term potentiation (LTP) or Long-Term depression (LTD) and induces two consolidation processes. The first is synaptic or cellular consolidation which involves remodeling synapses to produce enduring changes. Cellular consolidation is a short-term process that involves stabilizing the neural trace shortly after learning via structural brain changes in the hippocampus ( Lynch, 2004 ). The second is system consolidation, which builds on synaptic consolidation where reverberating activity leads to redistribution for long-term storage ( Mednick et al., 2011 ; Squire et al., 2015 ). System consolidation is a long-term process during which memories are gradually transferred to and integrated with cortical neurons, thus promoting their stability over time. In this way, memories are rendered less susceptible to forgetting. Hebb postulated that when two neurons are repeatedly activated simultaneously, they become more likely to exhibit a coordinated firing pattern of activity in the future ( Langille, 2019 ). This proposed enduring change in synchronized neuronal activation was consequently termed cellular consolidation ( Bermudez-Rattoni, 2010 ).

The following sections of this paper incorporate a more comprehensive investigation into various essential procedures connected with memory consolidation- namely: long-term potentiation (LTP), long-term depression (LTD), system consolidation, and cellular consolidation. Although these mechanisms have been presented briefly before this paragraph, the paper aims to offer greater insight into each process’s function within the individual capacity and their collective contribution toward memory consolidation.

Synaptic plasticity mechanisms implicated in memory stabilization

Long-Term Potentiation (LTP) and Long-Term Depression (LTP) are mechanisms that have been implicated in memory stabilization. LTP is an increase in synaptic strength, whereas LTD is a decrease in synaptic strength ( Ivanco, 2015 ; Abraham et al., 2019 ).

Long-Term Potentiation (LTP) is a phenomenon wherein synaptic strength increases persistently due to brief exposures to high-frequency stimulation ( Lynch, 2004 ). Studies of Long-Term Potentiation (LTP) have led to an understanding of the mechanisms behind synaptic strengthening phenomena and have provided a basis for explaining how and why strong connections between neurons form over time in response to stimuli.

The NMDA receptor-dependent LTP is the most commonly described LTP ( Bliss and Collingridge, 1993 ; Luscher and Malenka, 2012 ). In this type of LTP, when there is high-frequency stimulation, the presynaptic neuron releases glutamate, an excitatory neurotransmitter. Glutamate binds to the AMPA receptor on the postsynaptic neuron, which causes the neuron to fire while opening the NMDA receptor channel. The opening of an NMDA channel elicits a calcium ion influx into the postsynaptic neuron, thus initiating a series of phosphorylation events as part of the ensuing molecular cascade. Autonomously phosphorylated CaMKII and PKC, both actively functional through such a process, have been demonstrated to increase the conductance of pre-existing AMPA receptors in synaptic networks. Additionally, this has been shown to stimulate the introduction of additional AMPA receptors into synapses ( Malenka and Nicoll, 1999 ; Lynch, 2004 ; Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

There are two phases of LTP: the early phase and the late phase. It has been established that the early phase LTP (E-LTP) does not require RNA or protein synthesis; therefore, its synaptic strength will dissipate in minutes if late LTP does not stabilize it. On the contrary, late-phase LTP (L-LTP) can sustain itself over a more extended period, from several hours to multiple days, with gene transcription and protein synthesis in the postsynaptic cell ( Frey and Morris, 1998 ; Orsini and Maren, 2012 ). The strength of presynaptic tetanic stimulation has been demonstrated to be a necessary condition for the activation of processes leading to late LTP ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ). This finding is supported by research examining synaptic plasticity, notably Eric Kandel’s discovery that CREB–a transcription factor–among other cytoplasmic and nuclear molecules, are vital components in mediating molecular changes culminating in protein synthesis during this process ( Kaleem et al., 2011 ; Kandel et al., 2014 ). Further studies have shown how these shifts ultimately lead to AMPA receptor stabilization at post-synapses facilitating long-term potentiation within neurons ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

The “synaptic tagging and capture hypothesis” explains how a weak event of tetanization at synapse A can transform to late-LTP if followed shortly by the strong tetanization of a different, nearby synapse on the same neuron ( Frey and Morris, 1998 ; Redondo and Morris, 2011 ; Okuda et al., 2020 ; Park et al., 2021 ). During this process, critical plasticity-related proteins (PRPs) are synthesized, which stabilize their own “tag” and that from the weaker synaptic activity ( Moncada et al., 2015 ). Recent evidence suggests that calcium-permeable AMPA receptors (CP-AMPARs) are involved in this form of heterosynaptic metaplasticity ( Park et al., 2018 ). The authors propose that the synaptic activation of CP-AMPARs triggers the synthesis of PRPs, which are then engaged by the weak induction protocol to facilitate LTP on the independent input. The paper also suggests that CP-AMPARs are required during the induction of LTP by the weak input for the full heterosynaptic metaplastic effect to be observed ( Park et al., 2021 ). Additionally, it has been further established that catecholamines such as dopamine plays an integral part in memory persistence by inducing PRP synthesis ( Redondo and Morris, 2011 ; Vishnoi et al., 2018 ). Studies have found that dopamine release in the hippocampus can enhance LTP and improve memory consolidation ( Lisman and Grace, 2005 ; Speranza et al., 2021 ).

Investigations into neuronal plasticity have indicated that synaptic strength alterations associated with certain forms of learning and memory may be analogous to those underlying Long-Term Potentiation (LTP). Research has corroborated this notion, demonstrating a correlation between these two phenomena ( Lynch, 2004 ). The three essential properties of Long-Term Potentiation (LTP) that have been identified are associativity, synapse specificity, and cooperativity ( Kandel and Mack, 2013 ). These characteristics provide empirical evidence for the potential role of LTP in memory formation processes. Specifically, associativity denotes the amplification of connections when weak stimulus input is paired with a powerful one; synapse specificity posits that this potentiating effect only manifests on synaptic locations exhibiting coincidental activity within postsynaptic neurons, while cooperativity suggests stimulated neuron needs to attain an adequate threshold of depolarization before LTP can be induced again ( Orsini and Maren, 2012 ).

There is support for the idea that memories are encoded by modification of synaptic strengths through cellular mechanisms such as LTP and LTD ( Nabavi et al., 2014 ). The paper by Nabavi et al. (2014) shows that fear conditioning, a type of associative memory, can be inactivated and reactivated by LTD and LTP, respectively. The findings of the paper support a causal link between these synaptic processes and memory. Moreover, the paper suggests that LTP is used to form neuronal assemblies that represent a memory, and LTD could be used to disassemble them and thereby inactivate a memory ( Nabavi et al., 2014 ). Hippocampal LTD has been found to play an essential function in regulating synaptic strength and forming memories, such as long-term spatial memory ( Ge et al., 2010 ). However, it is vital to bear in mind that studies carried out on LTP exceed those done on LTD; hence the literature on it needs to be more extensive ( Malenka and Bear, 2004 ; Nabavi et al., 2014 ).

Cellular consolidation and memory

For an event to be remembered, it must form physical connections between neurons in the brain, which creates a “memory trace.” This memory trace can then be stored as long-term memory ( Langille and Brown, 2018 ). The formation of a memory engram is an intricate process requiring neuronal depolarization and the influx of intracellular calcium ( Mank and Griesbeck, 2008 ; Josselyn et al., 2015 ; Xu et al., 2017 ). This initiation leads to a cascade involving protein transcription, structural and functional changes in neural networks, and stabilization during the quiescence period, followed by complete consolidation for its success. Interference from new learning events or disruption caused due to inhibition can abort this cycle leading to incomplete consolidation ( Josselyn et al., 2015 ).

Cyclic-AMP response element binding protein (CREB) has been identified as an essential transcription factor for memory formation ( Orsini and Maren, 2012 ). It regulates the expression of PRPs and enhances neuronal excitability and plasticity, resulting in changes to the structure of cells, including the growth of dendritic spines and new synaptic connections. Blockage or enhancement of CREB in certain areas can affect subsequent consolidation at a systems level–decreasing it prevents this from occurring, while aiding its presence allows even weak learning conditions to produce successful memory formation ( Orsini and Maren, 2012 ; Kandel et al., 2014 ).

Strengthening weakly encoded memories through the synaptic tagging and capture hypothesis may play an essential role in cellular consolidation. Retroactive memory enhancement has also been demonstrated in human studies, mainly when items are initially encoded with low strength but later paired with shock after consolidation ( Dunsmoor et al., 2015 ). The synaptic tagging and capture theory (STC) and its extension, the behavioral tagging hypothesis (BT), have both been used to explain synaptic specificity and the persistence of plasticity ( Moncada et al., 2015 ). STC proposed that electrophysiological activity can induce long-term changes in synapses, while BT postulates similar effects of behaviorally relevant neuronal events on learning and memory models. This hypothesis proposes that memory consolidation relies on combining two distinct processes: setting a “learning tag” and synthesizing plasticity-related proteins ( De novo protein synthesis, increased CREB levels, and substantial inputs to nearby synapses) at those tagged sites. BT explains how it is possible for event episodes with low-strength inputs or engagements can be converted into lasting memories ( Lynch, 2004 ; Moncada et al., 2015 ). Similarly, the emotional tagging hypothesis posits that the activation of the amygdala in emotionally arousing events helps to mark experiences as necessary, thus enhancing synaptic plasticity and facilitating transformation from transient into more permanent forms for encoding long-term memories ( Richter-Levin and Akirav, 2003 ; Zhu et al., 2022 ).

Cellular consolidation, the protein synthesis-dependent processes observed in rodents that may underlie memory formation and stabilization, has been challenging to characterize in humans due to the limited ability to study it directly ( Bermudez-Rattoni, 2010 ). Additionally, multi-trial learning protocols commonly used within human tests as opposed to single-trial experiments conducted with non-human subjects suggest there could be interference from subsequent information that impedes individual memories from being consolidated reliably. This raises important questions regarding how individuals can still form strong and long-lasting memories when exposed to frequent stimuli outside controlled laboratory conditions. Although this phenomenon remains undiscovered by science, it is of utmost significance for gaining a deeper understanding of our neural capacities ( Genzel and Wixted, 2017 ).

The establishment of distributed memory traces requires a narrow temporal window following the initial encoding process, during which cellular consolidation occurs ( Nader and Hardt, 2009 ). Once this period ends and consolidation has been completed, further protein synthesis inhibition or pharmacological disruption will be less effective at altering pre-existing memories and interfering with new learning due to the stabilization of the trace in its new neuronal network connections ( Nader and Hardt, 2009 ). Thus, systems consolidation appears critical for the long-term maintenance of memory within broader brain networks over extended periods after their formation ( Bermudez-Rattoni, 2010 ).

System consolidation and memory

Information is initially stored in both the hippocampus and neocortex ( Dudai et al., 2015 ). The hippocampus subsequently guides a gradual process of reorganization and stabilization whereby information present within the neocortex becomes autonomous from that in the hippocampal store. Scholars have termed this phenomenon “standard memory consolidation model” or “system consolidation” ( Squire et al., 2015 ).

The Standard Model suggests that information acquired during learning is simultaneously stored in both the hippocampus and multiple cortical modules. Subsequently, it posits that over a period of time which may range from weeks to months or longer, the hippocampal formation directs an integration process by which these various elements become enclosed into single unified structures within the cortex ( Gilboa and Moscovitch, 2021 ; Howard et al., 2022 ). These newly learned memories are then assimilated into existing networks without interference or compression when necessary ( Frankland and Bontempi, 2005 ). It is important to note that memory engrams already exist within cortical networks during encoding. They only need strengthening through links enabled by hippocampal assistance-overtime allowing remote memory storage without reliance on the latter structure. Data appears consistent across studies indicating that both AMPA-and NMDA receptor-dependent “tagging” processes occurring within the cortex are essential components of progressive rewiring, thus enabling longer-term retention ( Takeuchi et al., 2014 ; Takehara-Nishiuchi, 2020 ).

Recent studies have additionally demonstrated that the rate of system consolidation depends on an individual’s ability to relate new information to existing networks made up of connected neurons, popularly known as “schemas” ( Robin and Moscovitch, 2017 ). In situations where prior knowledge is present and cortical modules are already connected at the outset of learning, it has been observed that a hippocampal-neocortical binding process occurs similarly to when forming new memories ( Schlichting and Preston, 2015 ). The proposed framework involves the medial temporal lobe (MTL), which is involved in acquiring new information and binds different aspects of an experience into a single memory trace. In contrast, the medial prefrontal cortex (mPFC) integrates this information with the existing knowledge ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 ). During consolidation and retrieval, MTL is involved in replaying memories to the neocortex, where they are gradually integrated with existing knowledge and schemas and help retrieve memory traces. During retrieval, the mPFC is thought to use existing knowledge and schemas to guide retrieval and interpretation of memory. This may involve the assimilation of newly acquired information into existing cognitive schemata as opposed to the comparatively slow progression of creating intercortical connections ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 , 2016 ).

Medial temporal lobe structures are essential for acquiring new information and necessary for autobiographical (episodic) memory ( Brown et al., 2018 ). The consolidation of autobiographical memories depends on a distributed network of cortical regions. Brain areas such as entorhinal, perirhinal, and parahippocampal cortices are essential for learning new information; however, they have little impact on the recollection of the past ( Squire et al., 2015 ). The hippocampus is a region of the brain that forms episodic memories by linking multiple events to create meaningful experiences ( Cooper and Ritchey, 2019 ). It receives information from all areas of the association cortex and cingulate cortex, subcortical regions via the fornix, as well as signals originating within its entorhinal cortex (EC) and amygdala regarding emotionally laden or potentially hazardous stimuli ( Sorensen, 2009 ). Such widespread connectivity facilitates the construction of an accurate narrative underpinning each remembered episode, transforming short-term into long-term recollections ( Richter-Levin and Akirav, 2003 ).

Researchers have yet to establish a consensus regarding where semantic memory information is localized within the brain ( Roldan-Valadez et al., 2012 ). Some proponents contend that such knowledge is lodged within perceptual and motor systems, triggered when we initially associate with a given object. This point of view is supported by studies highlighting how neural activity occurs initially in the occipital cortex, followed by left temporal lobe involvement during processing and pertinent contributions to word selection/retrieval via activation of left inferior frontal cortices ( Patterson et al., 2007 ). Moreover, research indicates elevated levels of fusiform gyrus engagement (a ventral surface region encompassing both temporal lobes) occurring concomitantly with verbal comprehension initiatives, including reading and naming tasks ( Patterson et al., 2007 ).

Research suggests that the hippocampus is needed for a few years after learning to support semantic memory (factual information), yet, it is not needed for the long term ( Squire et al., 2015 ). However, some forms of memory remain dependent on the hippocampus, such as the retrieval of spatial memory ( Wiltgen et al., 2010 ). Similarly, the Multiple-trace theory ( Moscovitch et al., 2006 ), also known as the transformation hypothesis ( Winocur and Moscovitch, 2011 ), posits that hippocampal engagement is necessary for memories that retain contextual detail such as episodic memories. Consolidation of memories into the neocortex is theorized to involve a loss of specific finer details, such as temporal and spatial information, in addition to contextual elements. This transition ultimately results in an evolution from episodic memory toward semantic memory, which consists mainly of gist-based facts ( Moscovitch et al., 2006 ).

Sleep and memory consolidation

Sleep is an essential physiological process crucial to memory consolidation ( Siegel, 2001 ). Sleep is divided into two stages: Non-rapid Eye Movement (NREM) sleep and Rapid Eye Movement (REM) sleep. NREM sleep is divided into three stages: N1, N2, and N3 (AKA Slow Wave Sleep or SWS) ( Rasch and Born, 2013 ). Each stage displays unique oscillatory patterns and phenomena responsible for consolidating memories in distinct ways. The first stage, or N1 sleep, is when an individual transitions between wakefulness and sleep. This type of sleep is characterized by low-amplitude, mixed-frequency brain activity. N1 sleep is responsible for the initial encoding of memories ( Rasch and Born, 2013 ). The second stage, or N2 sleep, is characterized by the occurrence of distinct sleep spindles and K-complexes in EEG. N2 is responsible for the consolidation of declarative memories ( Marshall and Born, 2007 ). The third stage of sleep N3, also known as slow wave sleep (SWS), is characterized by low-frequency brain activity, slow oscillations, and high amplitude. The slow oscillations which define the deepest stage of sleep are trademark rhythms of NREM sleep. These slow oscillations are delta waves combined to indicate slow wave activity (SWA), which is implicated in memory consolidation ( Tononi and Cirelli, 2003 ; Stickgold, 2005 ; Kim et al., 2019 ). Sleep spindles are another trademark defining NREM sleep ( Stickgold, 2005 ). Ripples are high-frequency bursts, and when combined with irregularly occurring sharp waves (high amplitude), they form the sharp-wave ripple (SWR). These spindles and the SWRs coordinate the reactivation and redistribution of hippocampus-dependent memories to neocortical sites ( Ngo et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). The third stage is also responsible for the consolidation of procedural memories, such as habits and motor skills ( Diekelmann and Born, 2010 ). During SWS, there is minimal cholinergic activity and intermediate noradrenergic activity ( Datta and MacLean, 2007 ).

Finally, the fourth stage of sleep is REM sleep, characterized by phasic REMs and muscle atonia ( Reyes-Resina et al., 2021 ). During REM sleep, there is high cholinergic activity, serotonergic and noradrenergic activity are at a minimum, and high theta activity ( Datta and MacLean, 2007 ). REM sleep is also characterized by local increases in plasticity-related immediate-early gene activity, which might favor the subsequent synaptic consolidation of memories in the cortex ( Ribeiro, 2007 ; Diekelmann and Born, 2010 ; Reyes-Resina et al., 2021 ). The fourth stage of sleep is responsible for the consolidation of emotional memories and the integration of newly acquired memories into existing knowledge structures ( Rasch and Born, 2013 ). Studies indicate that the cholinergic system plays an imperative role in modifying these processes by toggling the entire thalamo-cortico-hippocampal network between distinct modes, namely high Ach encoding mode during active wakefulness and REM sleep and low Ach consolidation mode during quiet wakefulness and NREM sleep ( Bergmann and Staresina, 2017 ; Li et al., 2020 ). Consequently, improving neocortical hippocampal communication results in efficient memory encoding/synaptic plasticity, whereas hippocampo-neocortical interactions favor better systemic memory consolidation ( Diekelmann and Born, 2010 ).

The dual process hypothesis of memory consolidation posits that SWS facilitates declarative, hippocampus-dependent memory, whereas REM sleep facilitates non-declarative hippocampus-independent memory ( Maquet, 2001 ; Diekelmann and Born, 2010 ). On the other hand, the sequential hypothesis states that different sleep stages play a sequential role in memory consolidation. Memories are encoded during wakefulness, consolidated during NREM sleep, and further processed and integrated during REM sleep ( Rasch and Born, 2013 ). However, there is evidence present that contradicts the sequential hypothesis. A study by Goerke et al. (2013) found that declarative memories can be consolidated during REM sleep, suggesting that the relationship between sleep stages and memory consolidation is much more complex than a sequential model. Moreover, other studies indicate the importance of coordinating specific sleep phases with learning moments for optimal memory retention. This indicates that the timing of sleep has more influence than the specific sleep stages ( Gais et al., 2006 ). The active system consolidation theory suggests that an active consolidation process results from the selective reactivation of memories during sleep; the brain selectively reactivates newly encoded memories during sleep, which enhances and integrates them into the network of pre-existing long-term memories ( Born et al., 2006 ; Howard et al., 2022 ). Research has suggested that slow-wave sleep (SWS) and rapid eye movement (REM) sleep have complementary roles in memory consolidation. Declarative and non-declarative memories benefiting differently depending on which sleep stage they rely on ( Bergmann and Staresina, 2017 ). Specifically, during SWS, the brain actively reactivates and reorganizes hippocampo-neocortical memory traces as part of system consolidation. Following this, REM sleep is crucial for stabilizing these reactivated memory traces through synaptic consolidation. While SWS may initiate early plastic processes in hippocampo-neocortical memory traces by “tagging” relevant neocortico-neocortical synapses for later consolidation ( Frey and Morris, 1998 ), long-term plasticity requires subsequent REM sleep ( Rasch and Born, 2007 , 2013 ).

The active system consolidation hypothesis is not the only mechanism proposed for memory consolidation during sleep. The synaptic homeostasis hypothesis proposes that sleep is necessary for restoring synaptic homeostasis, which is challenged by synaptic strengthening triggered by learning during wake and synaptogenesis during development ( Tononi and Cirelli, 2014 ). The synaptic homeostasis hypothesis assumes consolidation is a by-product of the global synaptic downscaling during sleep ( Puentes-Mestril and Aton, 2017 ). The two models are not mutually exclusive, and the hypothesized processes probably act in concert to optimize the memory function of sleep ( Diekelmann and Born, 2010 ).

Non-rapid eye movement sleep plays an essential role in the systems consolidation of memories, with evidence showing that different oscillations are involved in this process ( Düzel et al., 2010 ). With an oscillatory sequence initiated by a slow frontal cortex oscillation (0.5–1 Hz) traveling to the medial temporal lobe and followed by a sharp-wave ripple (SWR) in the hippocampus (100–200 Hz). Replay activity of memories can be measured during this oscillatory sequence across various regions, including the motor cortex and visual cortex ( Ji and Wilson, 2006 ; Eichenlaub et al., 2020 ). Replay activity of memory refers to the phenomenon where the hippocampus replays previously experienced events during sharp wave ripples (SWRs) and theta oscillations ( Zielinski et al., 2018 ). During SWRs, short, transient bursts of high-frequency oscillations occur in the hippocampus. During theta oscillations, hippocampal spikes are ordered according to the locations of their place fields during behavior. These sequential activities are thought to play a role in memory consolidation and retrieval ( Zielinski et al., 2018 ). The paper by Zielinski et al. (2018) suggests that coordinated hippocampal-prefrontal representations during replay and theta sequences play complementary and overlapping roles at different stages in learning, supporting memory encoding and retrieval, deliberative decision-making, planning, and guiding future actions.

Additionally, the high-frequency oscillations of SWR reactivate groups of neurons attributed to spatial information encoding to align synchronized activity across an array of neural structures, which results in distributed memory creation ( Swanson et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). Parallel to this process is slow oscillation or slow-wave activity within cortical regions, which reflects synced neural firing and allows regulation of synaptic weights, which is in accordance with the synaptic homeostasis hypothesis (SHY). The SHY posits that downscaling synaptic strengths help incorporate new memories by avoiding saturation of resources during extended periods–features validated by discoveries where prolonged wakefulness boosts amplitude while it diminishes during stretches of enhanced sleep ( Girardeau and Lopes-dos-Santos, 2021 ).

During REM sleep, the brain experiences “paradoxical” sleep due to the similarity in activity to wakefulness. This stage plays a significant role in memory processing. Theta oscillations which are dominant during REM sleep, are primarily observed in the hippocampus, and these are involved in memory consolidation ( Landmann et al., 2014 ). There has been evidence of coherence between theta oscillations in the hippocampus, medial frontal cortex, and amygdala, which support their involvement in memory consolidation ( Popa et al., 2010 ). During REM sleep, phasic events such as ponto-geniculo-occipital waves originating from the brainstem coordinate activity across various brain structures and may contribute to memory consolidation processes ( Rasch and Born, 2013 ). Research has suggested that sleep-associated consolidation may be mediated by the degree of overlap between new and already known material whereby, if the acquired information is similar to the information one has learned, it is more easily consolidated during sleep ( Tamminen et al., 2010 ; Sobczak, 2017 ).

In conclusion, understanding more about how the brains cycle through different stages of sleep, including specific wave patterns, offers valuable insight into the ability to store memories effectively. While NREM sleep is associated with SWRs and slow oscillations, facilitating memory consolidation and synaptic downscaling, REM sleep, characterized by theta oscillations and phasic events, contributes to memory reconsolidation and the coordination of activity across brain regions. By exploring the interactions between sleep stages, oscillations, and memory processes, one may learn more about how sleep impacts brain function and cognition in greater detail.

Century has passed since we addressed memory, and several notable findings have moved from bench-to-bedside research. Several cross-talks between multidiscipline have been encouraged. Nevertheless, further research is needed into neurobiological mechanisms of non-declarative memory, such as conditioning ( Gallistel and Balsam, 2014 ). Modern research indicates that structural change that encodes information is likely at the level of the synapse, and the computational mechanisms are implemented at the level of neural circuitry. However, it also suggests that intracellular mechanisms realized at the molecular level, such as micro RNAs, should not be discounted as potential mechanisms. However, further research is needed to study the molecular and structural changes brought on by implicit memory ( Gallistel and Balsam, 2014 ).

The contribution of non-human animal studies toward our understanding of memory processes cannot be understated; hence recognizing their value is vital for moving forward. While this paper predominantly focused on cognitive neuroscience perspectives, some articles cited within this paper were sourced from non-human animal studies providing fundamental groundwork and identification of critical mechanisms relevant to human memories. A need persists for further investigation—primarily with humans—which can validate existing findings from non-human animals. Moving forward, it is prudent for researchers to bridge the gap between animal and human investigations done while exploring parallels and exploring unique aspects of human memory processes. By integrating findings from both domains, one can gain a more comprehensive understanding of the complexities of memory and its underlying neural mechanisms. Such investigations will broaden the horizon of our memory process and answer the complex nature of memory storage.

This paper attempted to provide an overview and summarize memory and its processes. The paper focused on bringing the cognitive neuroscience perspective on memory and its processes. This may provide the readers with the understanding, limitations, and research perspectives of memory mechanisms.

Data availability statement

Author contributions.

SS and MKA: conceptualization, framework, and manuscript writing. AK: review and editing of the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We gratefully thank students and Indian Institute of Technology Roorkee (IITR) office staff for their conditional and unconditional support. We also thank the Memory and Anxiety Research Group (MARG), IIT Roorkee for its constant support.

Funding Statement

MKA was supported by the F.I.G. grant (IITR/SRIC/2741). The funding agency had no role in the preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Articles on Memory

Displaying 1 - 20 of 328 articles.

Candidates’ aging brains are factors in the presidential race − 4 essential reads

Jeff Inglis , The Conversation

Why forgetting is a normal function of memory – and when to worry

Alexander Easton , Durham University

Playing a musical instrument or singing in a choir may boost your brain – new study

Michael Hornberger , University of East Anglia

Wanting to ‘move on’ is natural – but women’s pandemic experiences can’t be lost to ‘lockdown amnesia’

Holly Thorpe , University of Waikato ; Grace O'Leary , University of Waikato ; Mihi Joy Nemani , University of Waikato , and Nida Ahmad , University of Waikato

The chickadee in the snowbank: A ‘canary in the coal mine’ for climate change in the Sierra Nevada mountains

Benjamin Sonnenberg , University of Nevada, Reno

A new supercomputer aims to closely mimic the human brain — it could help unlock the secrets of the mind and advance AI

Domenico Vicinanza , Anglia Ruskin University

Holocaust comparisons are overused – but in the case of Hamas’ Oct. 7 attack on Israel they may reflect more than just the emotional response of a traumatized people

Avinoam Patt , University of Connecticut and Liat Steir-Livny , Sapir Academic College

Lifestyle changes can reduce dementia risk by maintaining brain plasticity — but the time to act is now

Saskia Sivananthan , McGill University and Laura Middleton , University of Waterloo

Sharpeville: new research on 1960 South African massacre shows the number of dead and injured was massively undercounted

Nancy L Clark , Louisiana State University and William H. Worger , University of California, Los Angeles

Developmental amnesia: the rare disorder that causes children to forget things they’ve just learned

Rachael Elward , London South Bank University

How movies use music to manipulate your memory

Libby Damjanovic , Lund University

How collective memories fuel conflicts

Olumba E. Ezenwa , Royal Holloway University of London

Avoid cramming and don’t just highlight bits of text: how to help your memory when preparing for exams

Penny Van Bergen , University of Wollongong

How to manage exam season: don’t forget to take regular breaks and breathe

Paul Ginns , University of Sydney

What would you take with you? Why possessions matter in times of war and displacement

Hannah Wilson , Nottingham Trent University

The science of dreams and nightmares – what is going on in our brains while we’re sleeping?

Drew Dawson , CQUniversity Australia and Madeline Sprajcer , CQUniversity Australia

Why bilinguals may have a memory advantage – new research

Panos Athanasopoulos , Lancaster University

The science of why you can remember song lyrics from years ago

Kelly Jakubowski , Durham University

Alzheimer’s drug donanemab has been hailed as a ‘turning point’ for treatment. But what does it mean for people with the disease?

Steve Macfarlane , Monash University

Can a daily multivitamin improve your memory?

Jacques Raubenheimer , University of Sydney

Related Topics

• Alzheimer's disease
• Hippocampus
• Neuroscience

Top contributors

Senior lecturer, University of Adelaide

Head of Developmental Psychobiology Lab, Deakin University

Head of School of Education and Professor of Educational Psychology, University of Wollongong

Associate Professor in Music Psychology, Durham University

Pro Vice-Chancellor (Research Performance) and Professor of Cognitive Science, Macquarie University

Professor, Psychology, Lafayette College

Associate professor, University of Sydney

Professor of Clinical Neuropsychology, University of Cambridge

Vice Chancellor's Senior Research Fellow, Western Sydney University

Reader in Psychology, Aston University

Professor of Cognitive Psychology, City, University of London

Senior Lecturer, The University of Melbourne

Professor of Business Administration, Harvard University

Professor, Neuropsychology, University of Westminster

Professor, Faculty of Education and Arts, Australian Catholic University

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Memory News

Top headlines, latest headlines.

• Cognitive Health in Older People
• Fatty Food Before Surgery May Impair Memory
• Best Way to Memorize Stuff? It Depends ...
• Gene Involved in Vulnerability in Alzheimer's
• Brain Waves and Memories
• A Noninvasive Treatment for 'Chemo Brain'
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• One Brain Region for Familiarity and ...
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Earlier Headlines

Monday, february 26, 2024.

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• The Brain Is 'programmed' For Learning from People We Like

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• Improving Quality of Life and Sleep in People With Memory Problems Without Using Drugs

Monday, February 5, 2024

• Fatty Acids Hold Clue to Creating Memories

Thursday, February 1, 2024

• Scientists Discover a Potential Way to Repair Synapses Damaged in Alzheimer's Disease

Wednesday, January 31, 2024

• Polycystic Ovary Syndrome Tied to Memory, Thinking Problems
• Did Dementia Exist in Ancient Greek and Rome?

Monday, January 29, 2024

• Playing an Instrument Linked to Better Brain Health in Older Adults

Thursday, January 25, 2024

• Researchers Discover a New Role for a Protein That Helps Form Memories

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• Could Bizarre Visual Symptoms Be a Telltale Sign of Alzheimer's?

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• Don't Look Back: The Aftermath of a Distressing Event Is More Memorable Than the Lead-Up
• Study Reveals a Universal Pattern of Brain Wave Frequencies
• Third Major Study Finds Evidence That Daily Multivitamin Supplements Improve Memory and Slow Cognitive Aging in Older Adults
• Physical Exercise Boosts Motor Learning -- And Remembering What One Has Learned

Tuesday, January 16, 2024

• Amnesia Caused by Head Injury Reversed in Early Mouse Study

Wednesday, January 10, 2024

• Neuropsychological Effects of Rapid-Acting Antidepressants May Explain Their Clinical Benefits

Tuesday, January 2, 2024

• Researchers Identify New Coding Mechanism That Transfers Information from Perception to Memory

Monday, December 18, 2023

• AI's Memory-Forming Mechanism Found to Be Strikingly Similar to That of the Brain
• Memory Research: Breathing in Sleep Impacts Memory Processes

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Thursday, December 7, 2023

• Serotonin Loss May Contribute to Cognitive Decline in the Early Stages of Alzheimer's Disease
• Why Do Some Older Adults Show Declines in Their Spatial Memory?

Friday, December 1, 2023

• The World Needs More Empathy -- Here Is How Science Can Harness It

Thursday, November 30, 2023

• Distinct Brain Activity Triggered by Memories of Trauma
• Researchers Discover New Classes of RNA for Learning and Memory

Wednesday, November 29, 2023

• Pulling an All-Nighter? Don't Follow With an Important Decision

Tuesday, November 21, 2023

• How Do We Learn? Neuroscientists Pinpoint How Memories Are Likely to Be Stored in the Brain

Monday, November 20, 2023

• Why Emotions Stirred by Music Create Such Powerful Memories
• Nostalgia and Memories After Ten Years of Social Media

Thursday, November 16, 2023

• A Small Molecule Blocks Aversive Memory Formation, Providing a Potential Treatment Target for Depression
• Smaller Hippocampus Linked to Cognitive Decline

Tuesday, November 14, 2023

• Reducing 'vivid Imagery' That Fuels Addiction Cravings

Thursday, November 9, 2023

• Autism Brain States Hold the Key to Unlocking Childhood Memories, Findings Show

Wednesday, November 8, 2023

• Validating the Role of Inhibitory Interneurons in Memory

Monday, November 6, 2023

• Research Team Discovers New Role of Cerebellum in Coordinating the Brain Network Essential for Social Recognition Memory

Thursday, October 26, 2023

• Stunting in Infancy Linked to Differences in Cognitive and Brain Function

Monday, October 23, 2023

• New Study Reveals Role of Hippocampus in Two Functions of Memory

Tuesday, October 17, 2023

• Study Examines Role of Working Memory, Cognitive Functions in English Learners Learning to Write

Thursday, October 12, 2023

• Traumatic Memories Can Rewire the Brain

Wednesday, October 11, 2023

• New Study Finds Link Between Subjective and Objective Memory Decline

Monday, October 2, 2023

• In Forming Long-Term Memories, Vascular Cells Are Crucial

Thursday, September 28, 2023

• Protein P53 Regulates Learning, Memory, Sociability in Mice

Wednesday, September 27, 2023

• Saturated Fat May Interfere With Creating Memories in Aged Brain

Monday, September 25, 2023

• Brain Signals for Good Memory Performance Revealed

Friday, September 22, 2023

• Trigonelline Derived from Coffee Improves Cognitive Functions in Mice

Wednesday, September 13, 2023

• Potential New Approach to PTSD Treatment

Monday, September 11, 2023

• Antidepressants May Reduce Negative Memories While Improving Overall Memory

Wednesday, September 6, 2023

• The Sense of Order Distinguishes Humans from Other Animals

Wednesday, August 30, 2023

• Discoveries on Memory Mechanisms Could Unlock New Therapies for Alzheimer's and Other Brain Diseases

Wednesday, August 23, 2023

• A Fitness Tracker for Brain Health: How a Headband Can Identify Early Signs of Alzheimer's Disease in Your Sleep

Tuesday, August 22, 2023

• Which Is Easier to Remember, Symbols or Words?

Thursday, August 17, 2023

• Neuroscientists Successfully Test Theory That Forgetting Is Actually a Form of Learning
• More Than Meets the Eye: New Research Shows How the Visual System Contributes to Memory

Wednesday, August 16, 2023

• Certain Sugars Affect Brain 'plasticity,' Helping With Learning, Memory, Recovery

Monday, August 14, 2023

• Digital Puzzle Games Could Be Good for Memory in Older Adults
• The Anatomy of Memory: New Mnemonic Networks Discovered in the Brain

Wednesday, August 9, 2023

• 'Ebb and Flow' Brain Mechanism That Drives Learning Identified

Monday, August 7, 2023

• Memory, Forgetting, and Social Learning

Thursday, August 3, 2023

• Insulin-Like Hormones Critical for Brain Plasticity
• Potential New Tool for Early Identification of Dementia Risk

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Monday, July 31, 2023

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Wednesday, July 26, 2023

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Tuesday, July 25, 2023

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Tuesday, July 18, 2023

• AI-Guided Brain Stimulation Aids Memory in Traumatic Brain Injury

Friday, July 14, 2023

• Genes for Learning and Memory Are 650 Million Years Old

Monday, July 10, 2023

• Unraveling the Humanity in Metacognitive Ability: Distinguishing Human Metalearning from AI
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• Taking Good Care of Your Teeth May Be Good for Your Brain

Tuesday, June 27, 2023

• Glial Control of Parallel Memory Processing

Wednesday, June 14, 2023

• Tiny Device Mimics Human Vision and Memory Abilities

Thursday, June 1, 2023

• Discovery of Neurons That Allow Us to Recognize Others
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Monday, May 29, 2023

• Low-Flavanol Diet Drives Age-Related Memory Loss, Large Study Finds

Thursday, May 25, 2023

• Running Throughout Middle Age Keeps 'old' Adult-Born Neurons 'wired'

Wednesday, May 24, 2023

• People Who Live to Be 90+ With Superior Thinking Skills Are Resilient to Alzheimer's Pathology in Their Brains
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Monday, May 22, 2023

• Mind to Molecules: Does Brain's Electrical Encoding of Information 'tune' Sub-Cellular Structure?
• Effects on Memory of Neuron Diversity in Brain Region Revealed

Thursday, May 18, 2023

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Stanford University School of Medicine blog

What really happens to our memory as we age?

For anyone over the age of 30 reading this article, here's some bad news for you: Your brain is already on the decline.

The good(ish) news? From the brain's peak performance in our mid-20s, that decline is gradual, said Stanford neurologist Sharon Sha , MD. Despite common lore about aging and major lapses in memory, the effects of healthy aging on cognitive functions are actually quite subtle.

For example, a young or middle-aged adult can remember a sequence of seven numbers, on average, while a person in their 60s without dementia can hold onto six digits. When asked to list as many animals as they can in a short time frame, a skill known as verbal fluency, adults over 55 can list about 4% fewer than those under 55 years old.

Around a generation ago, we assumed that when we get older, we dramatically lose our memory. That's really not the case. Sharon Sha

"Around a generation ago, we assumed that when we get older, we dramatically lose our memory," said Sha. "That's really not the case."

For all the talk about age, memory and cognitive ability overloading the news cycle in this presidential election year, it seems like a good time to consult memory experts. Sha leads Stanford Medicine's Memory Disorders Division and divides her time between clinical work with patients who have Alzheimer's disease and other forms of dementia and leading clinical trials in patients with these conditions.

We asked her to discuss the links between aging and memory -- and what steps we can take to boost brain health. Her answers have been lightly edited for length and clarity.

What causes lapses in memory as we age?

Oh, that's a big bucket. Certain conditions like dementia, and specifically Alzheimer's disease, affect us more when we're older. The biggest risk factor for Alzheimer's disease is age. But beyond dementia, we think about a lot of other possible causes for memory lapses. When someone comes into the clinic and says they're having memory problems, we ask about medications, other psychiatric problems like anxiety and depression, and their sleep. There are a lot of factors that can affect memory and that are not necessarily expected in aging.

What's normal with memory and healthy aging, and what's not normal?

As we get older, we know we're going to get wrinkles and gray hair; similarly, there are normal age-related changes in our brains. Our processing speed -- how quickly we're thinking -- may slow down. The amount of content in our working memory may diminish. That short list of items you can keep in your head when going to the grocery story might get shorter, but it should not drop to zero. Although dementia is linked to age, it's not an inevitable part of getting older.

Although dementia is linked to age, it's not an inevitable part of getting older. Sharon Sha

How do you and other clinicians distinguish between normal aging and dementia?

There are screening tools that let us know if someone has a cognitive impairment that might indicate dementia. The definition of dementia also includes functional decline, meaning that someone is no longer able to live independently. If someone can no longer do their shopping or cooking, or remember to take their medications, that's concerning and beyond the expectation for normal aging.

There's also something we call mild cognitive impairment in which there is a cognitive decline from someone's baseline, but they are still functioning independently.

What happens to the brain when we lose memories?

We don't know exactly what happens biologically. But you can imagine that memory loss is part of the general atrophy and slowing down that happens to all parts of our bodies with age. We know there's a slight atrophy or shrinking of the brain with age, and that could include both a reduction in the volume and number of neurons as well as the insulation around neurons, called myelin. That loss of insulation also changes processing speed. And this is all normal -- just like you wouldn't expect to be as fast a runner at 80 as you were at 20.

When does the brain start that downward process?

It depends on the specific process, but generally speaking your brain is at its peak in terms of cognitive performance in your mid-20s. But if you're beyond your 20s, you probably recognize that you weren't making the best decisions at that point in your life. So, even if you're not at the peak of your brain function, what you have accrued now, if you're a couple decades beyond your mid-20s, is experience. It may take you longer to get to a decision, but that decision may be more likely to be right. I don't think any of us would trade our lived experience for a faster-working brain.

You probably recognize that you weren't making the best decisions (in your 20s). I don't think any of us would trade our lived experience for a faster-working brain. Sharon Sha

Are there things people can do to protect their memory and brain health?

This is the key question, because you can't fight aging, as much as you might want to. The benefit of aging is that you have all that experience, but how do we live and age healthfully? That's where research is supporting commonsense things like exercise. People often want to know what's the best kind of exercise. Any kind of exercise is better than sitting around. Aerobic exercise is the most studied in terms of brain health benefits, but smaller studies have also shown benefits for strength training and even being outside in nature. Just getting outside and moving your body is better than nothing.

We talk about cognitive stimulation, and anything that stimulates your brain in a positive way is great. If you hate crossword puzzles like I do, you're just going to get frustrated and that's not healthy, so pick something else. Learning a new sport, like pickleball, or a new kind of dance, is great for your brain because it's exercise, learning something new and giving you that social exposure.

We know from the pandemic that social isolation was bad for us, and part of it is that our brain needs that social interaction for fuel. In terms of actual fuel, the Mediterranean diet has been most well-studied in terms of brain health. However, if you are not of a Mediterranean background, culturally, you don't need to give up your food traditions. Just make sure you're getting those fruits and vegetables and lean proteins.

And finally, good sleep. If you have sleep apnea or other sleep problems, your brain is not getting what it needs to function at its best.

So, pretty much the stuff we know we should be doing, right?

Exactly. There's no easy pill, though everyone wants that magic prescription for brain health. It takes work. But it's never too late -- or too early -- to start taking care of your body and your brain.

Image: StunningArt

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The fact that some people remember the past as a series of episodes full of details (episodic memory), while others store in their brains the meaning of events (semantic memory), has a lot to do with the configuration of the connections in the brain , according to a recent study published in the journal Cortex. Neuroscience is deciphering the sophisticated mechanisms of human memory to explain how we file and remember information.

– Memory’s unreliable.

– Oh please!

– No, no, really! Memory’s not perfect. (…) Memory can change the shape of a room. It can change the color of a car and memories can be distorted. Memories are just an interpretation. They’re not a record. They’re irrelevant if you have the facts.

This is the conversation between Leonard and Teddy in the key scene of the movie Memento , one of the movies that best reflects the neuroscientific knowledge about memory. Its main character suffers from anterograde amnesia , which though it allows him to remember new words, he is unable to remember the recent past.

Specifically, “episodic memorizers” have more connections in the back regions of the brain where visual information and the perceptions of the senses are processed (occipital and parietal cortex), damaged in the case of Leonard. In contrast, the “semantic memorizers” show a denser neural network in the lower and middle part of the prefrontal region of the brain, predominantly conceptual and dedicated to organizing and prioritizing information.

This is not the only thing we have recently learned about how the human brain stores information. In addition to there being different types of memory, there are also different resolutions. At least two versions of each event are stored, a coarser one and a finer one, in different areas of the hippocampus , the seahorse-shaped area essential for memory and learning. And, as the occasion demands, from the same event we can recall the more general rough data or remember even the tiniest detail.

Another form of memory that has challenged neuroscientists for years is the  working memory , which is “the small amount of information that we can maintain temporarily in our memory, for tasks that we carry out at a given moment, in contrast to the huge amount of data archived in our permanent memory that we access from time to time,” as defined for OpenMind by Nelson Cowan , a researcher at the University of Missouri-Columbia (USA) and one of the world’s leading experts on this form of memory. “Among other things, we need our working memory to understand what we see, hear or read, as well as to solve problems,” he explains.

One of the most interesting aspects of memory that Cowan investigates is the relationship between working memory, attention and concentration. At the moment, we know that there are areas of the brain, such as the intrapariental groove , that deal with keeping certain information in the focus of attention, and that work as arrows “that point toward areas that hold visual or verbal information for a while,” says Cowan. What is less clear is to what extent we can still remember information to which we do not pay attention and of which we are not aware, one of the questions that the researcher wishes to clarify in the coming years.

If he had to highlight one challenge for neuroscientists in the study of memory for the next decade, Cowan would choose knowing what the limits of memory really are and how to overcome them . Some years ago, in 2001, the neuroscientist published an article in which he concluded that the basic temporal retention capacity of memory is 3 or 4 items for an adult and 2 or 3 for a child. However, it is also true that “humans manage to find ways to go beyond that limit using knowledge and strategies to combine information in specialized areas that make the human mind become more powerful and flexible,” adds Cowan.

What does seem clear at this point is the total capacity of long-term memory , which would be in the range of petabytes, in other words, equivalent to the capacity of the World Wide Web, according to a study by the Salk Institute. This is a ten times greater volume of information than previously thought. What is even more interesting is the discovery that, every 2 to 20 minutes, the synapses between neurons grow and shrink between 26 different sizes, depending on the signals they receive. That makes them extremely effective from a computational perspective, and very thrifty from an energy point of view. “Our discovery suggests that hidden beneath the apparent chaos and disorder, there is a surprising precision in the size and shape of neurons that we were completely ignorant of,” specifies Terry Sejnowski, co-author of the study. “The tricks of the brain hide the keys that we need to develop more efficient computers.”

And with so much information, what dictates the ordering of priorities of memories? Charan Ranganath and colleagues at the Center for Neuroscience at the University of California demonstrated using MRI that memory learns and prioritizes the recovery of that information which is related to some reward , and therefore it is expected that “it is useful to make future decisions that provide new rewards.”

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New Memory Research Teases 100x Density Jump, Merged Compute and Memory

A 10 to 100 times storage density jump? We'll take that as soon as possible, please.

New research along the frontiers of materials engineering holds promise for a truly astounding performance improvement for computing devices. A research team helmed by Markus Hellbrand et al. and associated with the University of Cambridge believes the new material, based of hafnium oxide layers tunneled by voltage-changing barium spikes, fuses the properties of memory and processing-bound materials. That means the devices could work for data storage, offering anywhere from 10 to 100 times the density of existing storage mediums, or it could be used as a processing unit. 

Published in the Science Advances journal , the research gives us a road through which we might end with far greater density, performance and energy efficiency in our computing devices. So much so, in fact, that a typical USB stick based on the technology (which is called continuous range ) could hold between 10 and 100 times more information than the ones we currently use.

With RAM doubling in density every four years, as pointed out by JEDEC , it'd take RAM makers decades to eventually achieve the same level of density as this technology has shown today.

The device is also a light at the tunnel of neuromorphic computing. Like the neurons in our brain, the material (known as a resistive switching memory ) holds the promise of working as both a storage and processing medium. That's something that simply doesn't happen in our current semiconductor technology: the transistor and materials design arrangements are so different between what you need for a memory cell and what you need for a processing one (mainly in terms of endurance, as in, the ability not to suffer performance degradations) that there's currently no way to merge them.

This inability to merge them means that information must be continuously flowing between the processing system and its various caches ( when thinking of a modern CPU ), as well as its external memory pool (looking at you, best DDR5 kits on the market ). in computing, this is known as von Neumann's bottleneck, meaning that a system with separate memory and processing capabilities will be fundamentally limited by the bandwidth between them both (what's usually known as the bus). This is why all semiconductor design companies (from Intel through AMD, Nvidia, and many others) design dedicated hardware that accelerates this exchange of information, such as Infinity Fabric and NVLink.

The problem is that this exchange of information has an energy cost, and this energy cost is currently limiting the upper bounds of achievable performance. Remember that when energy circulates, there are also inherent losses, which result in increased power consumption (a current hard limit on our hardware designs and a growing priority in semiconductor design) as well as heat — yet another hard limit that's led to the development of increasingly exotic cooling solutions to try and allow Moore's law to limp ahead for a while yet. Of course, there's also the sustainability factor: it's expected that computing will consume as much as 30% of the worldwide energy needs in the not-so-distant future.

“To a large extent, this explosion in energy demands is due to shortcomings of current computer memory technologies,” said first author Dr. Markus Hellenbrand, from Cambridge’s Department of Materials Science and Metallurgy. “In conventional computing, there’s memory on one side and processing on the other, and data is shuffled back between the two, which takes both energy and time.”

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The benefits of merging both memory and processing are quite spectacular, as you might imagine. While conventional memory is capable of just two states (one or zero, the cause for the "binary" nomenclature), a resistive switching memory device can change its resistance through a range of states. This allows it to function at increased varieties of voltages, which in turn allows for more information to be encoded. At a high enough level, this is much the same process happening in the NAND realm, with increases in bits per cell corresponding to a higher number of possible voltage states unlocked in the memory cell's design.

One way to differentiate processing from storing is saying that processing means that the information is undergoing writes and rewrites (additions or subtractions, transformations or reorganizations) as fast as its switching cycle is requested to. Storing means that the information needs to be static for a longer period of time — perhaps because it's part of the Windows or Linux kernels, for instance.

To build these synapse devices , as the paper refers to them, the research team had to find a way to deal with a materials engineering bottleneck known as the uniformity problem. Because hafnium oxide (HfO2) doesn't possess any structure at the atomic level, the hafnium and oxygen atoms that can make or break its insulating properties are deposited haphazardly. This limits its application for conducting electrons (electrical power); the more ordered the atomic structure is, the least resistance will be caused, so the higher the speed and efficiency. But the team found that depositing barium (Ba) within the thin films of unstructured hafnium oxide resulted in highly-ordered barium bridges (or spikes). And because their atoms are more structured, these bridges can better allow the flow of electrons.

But the fun began when the research team found they could dynamically change the height of the barium spikes, allowing for fine-grained control of their electrical conductivity. They found that the spikes could offer switching capabilities at a rate of ~20ns, meaning that they could change their voltage state (and thus hold different information) within that window. They found switching endurances of >10^4 cycles, with a memory window >10. This means that while the material is fast, the maximum number of voltage state changes it can currently withstand stands at around 10,000 cycles - not a terrible result, but not an amazing one.

It's equivalent to the endurance available with MLC (Multi-Level Cell) technology, which will naturally limit its application - the usage of this material as a processing medium (where voltage states are rapidly changed in order to keep a store of calculations and their intermediate results).

Doing some rough napkin math, the ~20 ns switching leads to an operating frequency of 50 MHz ( converting to cycles per nanosecond ). With the system processing different states at full speed (working as a GPU or CPU, for instance), that means the barium bridges would cease functioning (hit their endurance limit) at around the 0,002-second mark (remember, it's only operating at 50 MHz). That doesn't seem like it could be performant enough for a processing unit.

But for storage? Well, that's where the USB stick that's "10 to 100 times denser" in terms of memory capacity comes in. These synapse devices can access a lot more intermediate voltage states than even the densest NAND technology in today's roomiest USB sticks can - by a factor of 10 or 100.

Who wouldn't love to have a 10 TeraByte or even 100 TeraByte "USB 7" stick on their hands?

There's some work to be done in terms of endurance and switching speed of the barium bridges, but it seems like the design is already an enticing proof of concept. Better yet, the semiconductor industry already works with hafnium oxide, so there are fewer tooling and logistics nightmares to fight through.

But here's a particularly ingenious product possibility: imagine that the technology improves to the point that it's fabricated and useable to design an AMD or Nvidia GPU (which these days operate at around the 2 GHz mark). There's a world where that graphics card comes with a reset factory state where it's entirely operating as memory (now imagine a graphics card with 10 TB of it, the same as our hypothetical USB stick).

Imagine a world where what AMD and Nvidia offered were essentially programmable GPUs, with continuous range-based GPU dies product-stacked in terms of maximum storage capability (remember the 10 to 100 denser than current USB). If you are an AI aficionado attempting to build your own Large Language Model (LLM), you can program your GPU so that just the right amount of these synthetic devices, these neuromorphic transistors, runs processing functions — there's no telling how many trillion parameters models will eventually end up as their complexity increases, so memory will grow increasingly more important.

Being able to dictate whether the transistors in your graphics card are used exactly as memory or exactly as eye-candy-amplifiers to turn graphics settings up to eleven, that'd be entirely up to the end-user; from casual gamer to High Performance Computing (HPC) installer. Even if that meant a measured decay in the longevity of parts of our chip.

We're always upgrading them anyway, aren't we?

But let's not get ahead of ourselves. Even though this isn't as dangerous an issue as AI development and its regulation, there's little to be gained in dreaming so far ahead. Like all technology, it'll come - when it's ready. if it ever is.

Francisco Pires is a freelance news writer for Tom's Hardware with a soft side for quantum computing.

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• jeremyj_83 I'd take a cheap way to have 64GB or more RAM in my computer. It would be even more amazing to have cheap 1TB DIMMs in the server world. Reply
• Kamen Rider Blade Given it's limited switching endurances of >10^4 cycles = 10,000 cycles. It's best used as a replacement for NAND Flash, can it get to the cheap costs that NAND Flash is currently at? How long can it hold data in a off-line state? Many people would be happy with 10,000 cycles Endurance given how abysmal QLC is right now. Reply

Kamen Rider Blade said: Given it's limited switching endurances of >10^4 cycles = 10,000 cycles. It's best used as a replacement for NAND Flash, can it get to the cheap costs that NAND Flash is currently at? How long can it hold data in a off-line state? Many people would be happy with 10,000 cycles Endurance given how abysmal QLC is right now.

jeremyj_83 said: Even with 1k cycles that is still plenty for even data center drives. With the increase in storage amount you get a big increase in endurance. The Solidigm 7.68TB QLC drive has a 5.9PB endurance. https://www.servethehome.com/solidigm-has-a-61-44tb-ssd-coming-this-quarter/

• usertests With a 100x density increase and a bonus recovery of write endurance, you could talk about maxing out the SDUC/microSDUC standard (128 TB/TiB). Reply
• gg83 It's the compute on memory that I'm most excited about. Merging the two is for sure the future. How much cache is being slapped on top on AMD chips now? Might as well build a tech that combines the two right? But this seems to be an "either-or" process/memory tech huh? Reply

gg83 said: It's the compute on memory that I'm most excited about. Merging the two is for sure the future. How much cache is being slapped on top on AMD chips now? Might as well build a tech that combines the two right? But this seems to be an "either-or" process/memory tech huh?

Kamen Rider Blade said: Imagine how much nicer it would be at 10k cycles, it'd be like the old days of SLC/MLC NAND flash, but with much better bit density.

jeremyj_83 said: Tell me on the desktop will you notice any difference from 10PB or write endurance vs 1PB? No. These new QLC drives probably have more write endurance than the 80GB old SLC drives from 2010 even with fewer write cycles.

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Fridge magnets have important pull for holiday memories, says research

by University of Liverpool

New University of Liverpool research has shown fridge magnets are more than just tourist souvenirs providing holidaymakers with an important aide for memory recall.

Research from University of Liverpool Management School found that these holiday trinkets can be unique in evoking a diverse range of memories—with magnets found to boost travelers' moods long after a holiday has ended.

Within the study, published in the journal Annals of Tourism Research , one participant said, "If you go to any place, obviously in a day you could take 50 to 100 photos…Whereas now I don't tend to take a picture of anything…I'll just get a fridge magnet at the end, and I can remember it all from [that]…How many people flick through their old photos on their phone? You couldn't. Unless you've got an electronic, sort of photo thing you can have on the side constantly changing the images on a daily basis or whatever. It's a fridge magnet for me."

Of the 19 participants who were interviewed, many said they were reminded of their holidays every time they opened the fridge door. Some magnets had become attached to more poignant memories over the years, serving as a reminder of trips taken with friends or family members who had since died.

Dr. John Byrom Associate Dean at the University of Liverpool Management School said, "Far from being banal 'tat,' our study found fridge magnets can bring a past holiday experience to life more powerfully than many other types of tourist souvenir.

"Little research has taken place looking at what happens to fridge magnet souvenirs after people come back from holiday, so it was really interesting to talk to participants about their different experiences. It was clear that when people talked through what their magnets meant to them, they were very easily able to generate these memories and responses of very specific events or people, including quite poignant examples of holidays that they've had with people who have died or children who have grown up and moved away.

"It was also very interesting how fridge magnets can be used as a means of forgetting things that had been bad in your life, to reflect on how things got better."

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ChatGPT Use Linked to Memory Loss, Procrastination in Students

Brain drain.

N ew research has found a worrying link to memory loss and tanking grades in students who relied on ChatGPT, in an early but fascinating exploration of the swift impact that large language models have had in education .

As detailed in a new study  published in the International Journal of Educational Technology in Higher Education, the researchers surveyed hundreds of university students — ranging from undergrads to doctoral candidates — over two phases, using self-reported evaluations. They were spurred on by witnessing more and more of their own students turn to ChatGPT.

"My interest in this topic stemmed from the growing prevalence of generative artificial intelligence in academia and its potential impact on students," study co-author Muhammad Abhas at the National University of Computer and Emerging Sciences in Pakistan told PsyPost . "For the last year, I observed an increasing, uncritical, reliance on generative AI tools among my students for various assignments and projects I assigned."

Show of Hands

In the first phase, the researchers collected responses from 165 students who used an eight-item scale to report their degree of ChatGPT reliance. The items ranged from "I use ChatGPT for my course assignments" to "ChatGPT is part of my campus life."

To validate those results, they also conducted a more rigorous "time-lagged" second phase, in which they expanded their scope to nearly 500 students, who were surveyed three times at one to two week intervals.

Perhaps unsurprisingly, the researchers found that students under a heavy academic workload and "time pressure" were much more likely to use ChatGPT. They observed that those who relied on ChatGPT reported more procrastination, more memory loss, and a drop in GPA. And the reason why is quite simple: the chatbot, however good or bad its responses are, is making schoolwork too easy.

"Since ChatGPT can quickly respond to any questions asked by a user," the researchers wrote in the study, "students who excessively use ChatGPT may reduce their cognitive efforts to complete their academic tasks, resulting in poor memory."

Two Way Street

There were a few curveballs, however.

"Contrary to expectations, students who were more sensitive to rewards were less likely to use generative AI," Abbas told PsyPost , suggesting that those seeking good grades avoided using the chatbot out of fear of getting caught.

It's possible that the relationship between ChatGPT usage and its negative effects is bidirectional, notes PsyPost . A student may turn to the chatbot because they already have bad grades, and not the other way around. It's also worth considering that the data was self-reported, which comes with its own biases.

That's not to exonerate AI, though. Based on these findings, we should be wary about ChatGPT's role in education.

"The average person should recognize the dark side of excessive generative AI usage," Abbas told Psypost . "While these tools offer convenience, they can also lead to negative consequences such as procrastination, memory loss, and compromised academic performance."

More on AI: Google's AI Search Caught Pushing Users to Download Malware

The post ChatGPT Use Linked to Memory Loss, Procrastination in Students appeared first on Futurism .

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Microsoft Research Blog

Introducing garnet – an open-source, next-generation, faster cache-store for accelerating applications and services.

Published March 18, 2024

By Badrish Chandramouli , Partner Research Manager

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Researchers at Microsoft have been working for nearly a decade to address the increasing demand for data storage mechanisms to support the rapid advances in interactive web applications and services. Our new cache-store system called Garnet, which offers several advantages over legacy cache-stores, has been deployed in multiple use cases at Microsoft, such as those in the Windows & Web Experiences Platform, Azure Resource Manager, and Azure Resource Graph, and is now available as an open-source download at https://github.com/microsoft/garnet (opens in new tab) . In open sourcing Garnet, we hope to enable the developer community to benefit from its performance gains and capabilities, to build on our work, and to expand the Garnet ecosystem by adding new API calls and features. We also hope that the open sourcing will encourage follow-up academic research and open future collaboration opportunities in this important research area.

The cache-store problem

The growth of cloud and edge computing has brought an increasing number and range of applications and services that need to access, update, and transform data with higher efficiency, lower latencies, and lower costs than ever before. These applications and services often require significant operational spending on storage interactions, making this one of the most expensive and challenging platform areas today. A cache-store software layer, deployed as a separately scalable remote process, can ease these costs and improve application performance. This has fueled a growing cache-store industry, including many open-source systems, such as Redis, Memcached, KeyDB, and Dragonfly.

Unlike traditional remote cache-stores, which support a simple get/set interface, modern caches offer rich APIs and feature sets. They support raw strings, analytic data structures such as Hyperloglog, and complex data types such as sorted sets and hash. They allow users to checkpoint and recover the cache, create data shards, maintain replicated copies, and support transactions and custom extensions.

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However, existing systems achieve this feature richness at a cost, by keeping the system design simple, which limits the ability to fully exploit the latest hardware capabilities (e.g., multiple cores, tiered storage, fast networks). Further, many of these systems are not explicitly designed to be easily extensible by app developers or to work well on diverse platforms and operating systems.

Introducing Garnet

At Microsoft Research, we have been investigating modern key-value database architectures since 2016. Our prior work, the FASTER (opens in new tab) embedded key-value library, which we open-sourced (opens in new tab) in 2018, demonstrated orders-of-magnitude better performance than existing systems, while focusing on the simple single-node in-process key-value model.

Starting in 2021, based on requirements from use-cases at Microsoft, we began building a new remote cache-store with all the necessary features to serve as a viable replacement to existing cache-stores. Our challenge was to maintain and enhance the performance benefits that we achieved in our earlier work, but in this more general and realistic network setting.

The result of this effort is Garnet – a new cache-store that offers several unique benefits:

• Garnet adopts the popular RESP wire protocol as a starting point, which makes it possible to use Garnet from unmodified Redis clients available in most programming languages today.
• Garnet offers much better scalability and throughput with many client connections and small batches, leading to cost savings for large apps and services.
• Garnet demonstrates better client latency at the 99 th and 99.9 th percentiles, which is critical to real-world scenarios.
• Based on the latest .NET technology, Garnet is cross-platform, extensible, and modern. It is designed to be easy to develop for and evolve, without sacrificing performance in the common case. We leveraged the rich library ecosystem of .NET for API breadth, with open opportunities for optimization. Thanks to our careful use of .NET, Garnet achieves state-of-the-art performance on both Linux and Windows.

API features: Garnet supports a wide range of APIs including raw string, analytical, and object operations described earlier. It also implements a cluster mode with sharding, replication, and dynamic key migration. Garnet supports transactions in the form of client-side RESP transactions (opens in new tab) and our own server-side stored procedures in C# and allows users to define custom operations on both raw strings and new object types, all in the convenience of C#, leading to a lower bar for developing custom extensions.

Network, storage, cluster features: Garnet uses a fast and pluggable network layer, enabling future extensions such as leveraging kernel-bypass stacks. It supports secure transport layer security (TLS) communications as well as basic access control. Garnet’s storage layer, called Tsavorite, was forked from OSS FASTER, and includes strong database features such as thread scalability, tiered storage support (memory, SSD, and cloud storage), fast non-blocking checkpointing , recovery, operation logging for durability, multi-key transaction support, and better memory management and reuse. Finally, Garnet supports a cluster mode of operation – more on this later. 

Performance preview

We illustrate a few key results comparing Garnet to leading open-source cache-stores. A more detailed performance comparison can be found on our website at https://microsoft.github.io/garnet/ (opens in new tab) .

We provision two Azure Standard F72s v2 virtual machines (72 vcpus, 144 GiB memory each) running Linux (Ubuntu 20.04), with accelerated TCP enabled. One machine runs different cache-store servers, and the other is dedicated to issuing workloads. We use our own benchmarking tool, called Resp.benchmark (opens in new tab) , to generate all results. We compare Garnet to the latest open-source versions of Redis (opens in new tab) (v7.2), KeyDB (opens in new tab) (v6.3.4), and Dragonfly (opens in new tab) (v6.2.11). We use a uniform random distribution of keys in these experiments (Garnet’s shared memory design benefits even more with skewed workloads). The data is pre-loaded onto each server, and fits in memory in these experiments.

Experiment 1: Throughput with varying number of client sessions

We start with large batches of GET operations (4096 requests per batch) and small payloads (8-byte keys and values) to minimize network overhead and compare the systems as we increase the number of client sessions. We see from Figure 1 that Garnet exhibits better scalability than Redis and KeyDB, while achieving higher throughput than all three baseline systems (the y-axis is log scale). Note that, while Dragonfly shows similar scaling behavior as Garnet, it is a pure in-memory system. Further, Garnet’s throughput relative to other systems remains strong when the database size (i.e., the number of distinct keys pre-loaded) is significantly larger, at 256 million keys, than what would fit in the processor caches.

Experiment 2: Throughput with varying batch sizes

We next vary the batch size, with GET operations and a fixed number (64) of client sessions. We experiment with two different database sizes as before. Figure 2 shows that Garnet performs better even with no batching, and the gap increases even for very small batch sizes. Payload sizes are the same as before. Again, the y-axis is log scale.

Experiment 3: Latency with varying number of client sessions

We next measure client-side latencies for the various systems. Figure 3 shows that, as we increase the number of client sessions, Garnet’s latency (measured in microseconds) at various percentiles stays much more stable and lower as compared to other systems. Here, we issue a mix of 80% GET and 20% SET operations, with no operation batching.

Experiment 4: Latency with varying batch sizes 

Garnet’s latency is optimized for adaptive client-side batching and many sessions querying the system. We increase the batch sizes from 1 to 64 and plot latency at different percentiles below with 128 active client connections. We see in Figure 4 that Garnet’s latency is low across the board. As before, we issue a mix of 80% GET and 20% SET operations. 

Other experiments

We have also experimented with other features and operation types and found Garnet to perform and scale well. Our documentation (opens in new tab) has more details, including how to run these experiments so that you can see the benefits for your own use cases.

Garnet’s design highlights

Garnet’s design re-thinks the entire cache-store stack – from receiving packets on the network, to parsing and processing database operations, to performing storage interactions. We build on top of years of research, with over 10 research papers published over the last decade. Figure 5 shows Garnet’s overall architecture. We highlight a few key ideas below.

Garnet’s network layer inherits a shared memory design inspired by our prior research on ShadowFax . TLS processing and storage interactions are performed on the IO completion thread, avoiding thread switching overheads in the common case. This approach allows CPU cache coherence to bring the data to the network, instead of traditional shuffle-based designs, which require data movement on the server.

Garnet’s storage design consists of two Tsavorite key-value stores whose fates are bound by a unified operation log. The first store, called the “main store,” is optimized for raw string operations and manages memory carefully to avoid garbage collection. The second, and optional, “object store” is optimized for complex objects and custom data types, including popular types such as Sorted Set, Set, Hash, List, and Geo. Data types in the object store leverage the .NET library ecosystem for their current implementations. They are stored on the heap in memory (which makes updates very efficient) and in a serialized form on disk. In the future, we plan to investigate using a unified index and log to ease maintenance.

A distinguishing feature of Garnet’s design is its narrow-waist Tsavorite storage API, which is used to implement the large, rich, and extensible RESP API surface on top. This API consists of read, upsert, delete, and atomic read-modify-write operations, implemented with asynchronous callbacks for Garnet to interject logic at various points during each operation. Our storage API model allows us to cleanly separate Garnet’s parsing and query processing concerns from storage details such as concurrency, storage tiering, and checkpointing. 

Garnet further adds support for multi-key transactions based on two-phase locking. One can either use RESP client-side transactions (MULTI/EXEC) or use our server-side transactional stored procedures in C#.

Cluster mode

In addition to single-node execution, Garnet supports a cluster mode, which allows users to create and manage a sharded and replicated deployment. Garnet also supports an efficient and dynamic key migration scheme to rebalance shards. Users can use standard Redis cluster commands to create and manage Garnet clusters, and nodes perform gossip to share and evolve cluster state. Overall, Garnet’s cluster mode is a large and evolving feature, and we will cover more details in subsequent posts.

Looking ahead

As Garnet is deployed in additional scenarios, we will continue to share those details in future articles. We also look forward to continuing to add new features and improvements to Garnet, as well as working with the open-source community.

Project contributors

Garnet Core: Badrish Chandramouli , Vasileios Zois , Lukas Maas , Ted Hart, Gabriela Martinez Sanchez, Yoganand Rajasekaran , Tal Zaccai , Darren Gehring , Irina Spiridonova . 

Collaborators: Alan Yang, Pradeep Yadav, Alex Dubinkov, Venugopal Latchupatulla, Knut Magne Risvik , Sarah Williamson, Narayanan Subramanian, Saurabh Singh, Padmanabh Gupta, Sajjad Rahnama, Reuben Bond, Rafah Hosn , Surajit Chaudhuri , Johannes Gehrke , and many others.

Related publications

Faster: a concurrent key-value store with in-place updates, concurrent prefix recovery: performing cpr on a database, achieving high throughput and elasticity in a larger-than-memory store, meet the authors.

Badrish Chandramouli

Partner Research Manager

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