{"id":218904,"date":"2024-10-14T00:00:35","date_gmt":"2024-10-14T04:00:35","guid":{"rendered":"https:\/\/www.thetransmitter.org\/?p=218904"},"modified":"2024-10-14T14:35:58","modified_gmt":"2024-10-14T18:35:58","slug":"what-makes-memories-last-dynamic-ensembles-or-static-synapses","status":"publish","type":"post","link":"https:\/\/www.thetransmitter.org\/the-big-picture\/what-makes-memories-last-dynamic-ensembles-or-static-synapses\/","title":{"rendered":"What makes memories last\u2014dynamic ensembles or static synapses?"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"

Teasing out how different subfields conceptualize central terms might help move this long-standing debate forward. I asked eight scientists to weigh in.<\/p>\n","protected":false},"author":2,"featured_media":218920,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":""},"categories":[139],"tags":[225,711,63,49,167,319],"acf":{"primary_tag":319,"doi_url":"https:\/\/doi.org\/10.53053\/KUCY6534","custom_js_library":"","hero_type":"feat_image","hero_alt_image":null,"hero_youtube":"","hero_video":null,"hero_layout":"full","hero_caption":"Conceptual chasm:<\/b> Scientists debate how information is represented and stored in the brain.","hero_by":"Illustration by","hero_credit":211270,"hero_bg_color":"none","authors":[107456],"other_authors":"","related_title":"Explore more from The Transmitter<\/em>","related_hide":false,"related_filter":"latest","related_tag":null,"related_category":null,"related_custom":{"articles":null},"recommended_title":"Recommended reading","recommended_hide":false,"recommended_filter":"latest","recommended_tag":null,"recommended_category":null,"recommended_custom":{"articles":null},"comps":[{"acf_fc_layout":"copy_comp","copy":"\u201cRemembrance of things past is not necessarily the remembrance of things as they were.\u201d \u2014Marcel Proust<\/em><\/strong>\r\n\r\nOur subjective experiences are continuously filtered through the lens of memory, so brains have to find a balance between stable memory storage and the flexibility to update existing memories with new learning. On that much, neuroscientists agree. But a conceptual chasm has emerged in connecting the molecular processes that convert experiences into long-lasting stable changes in the brain and how information is represented and stored in the brain. This may be due, in part, to differences in how these concepts are defined.\r\n\r\nThe debate over how information is stored in the brain is often represented as one between two extremes. One viewpoint posits that learning induces changes in gene expression that ultimately alter the structure and function of specific synapses within the physical memory circuit, or engram. These molecular changes at the synapses can remain stable for the lifetime of the memory. The other viewpoint claims that information is represented not in a specific set of cells or synapses but rather across a loose set of cells and circuits that \u201cdrift\u201d over time. In this view, the cells that initially encoded an experience are not the same set of cells that actually store the information. Indeed, the precise set of cells do not matter in this framework\u2014the information for a specific memory is instead decoded from the computational space of firing patterns across a set of cells.\r\n\r\nThe debate\u2014often given the shorthand of \u201cmemory engrams versus representational drift\u201d\u2014has been the subject of numerous conference sessions, including one I co-organized in Utah in January. What these discussions have made clear is that the debate over how memory is stored runs deeper than reconciling different levels of analysis. Scientists disagree over the language that describes even the basic concepts, making it challenging to engage in meaningful discourse. Our discussion panel, for example, brought together memory scientists working at different levels of analysis to explore the various concepts of memory, ranging from molecular storage events, such as stable synapses, to representational drift in neural circuits. In planning the panel, it became clear that we needed to agree on how we talk about concepts such as \u201cstorage\u201d and \u201cdrift.\u201d Molecular neuroscientists think of storage in concrete terms, such as a set of synapses that are strengthened to form an engram. Systems neuroscientists, by contrast, think in more abstract terms, in which information is represented as computations performed by neural circuits.\r\n\r\nThe case for memory engrams<\/strong>\r\n\r\nCurrent cellular models of learning and memory have focused on cell-autonomous mechanisms, such as long-term potentiation and depression, which strengthen or weaken synaptic weights to sculpt the specific circuits that store memories. Learning induces rapid gene expression of a set of immediate early genes (IEGs), which help orchestrate several processes involved in plasticity and information storage. The promoter sequences that make these IEGs responsive to neuronal activity and experience have been used to \u201ctag\u201d the cells that are active during learning. In experiments that used this approach, light-sensitive receptors were expressed only in the cells active during learning. Shining a light to activate these cells days or even weeks after training resulted in the recall of a memory without any external experience or cue. This remarkable observation set the stage for the idea that \u201cengram\u201d neurons that encode learning are sufficient to store and recall a memory. But these manipulations are cell-wide, not targeted to specific synapses. More definitive evidence for the role of synapses will require higher-resolution tools that can tag specific synapses active during learning. It also remains unclear if these cells are storing the information or simply triggering recall through another set of cells, where the information is actually stored.\r\n\r\nThe representational drift perspective<\/strong>\r\n\r\nInformation received from the outside world is represented internally in the brain through specific firing patterns. Though neurons that are active during a particular experience may initially encode information, long-term recording of neuronal activity shows that the firing patterns of these cells change over time in response to the same stimuli. Thus, the internal representation of information is thought to \u201cdrift.\u201d The classic illustration of drift comes from hippocampal place cells, which fire in specific locations as an animal moves through space. Over time, the place cells encoding specific locations change, despite the environment staying the same.\r\n\r\nAlthough drift has been observed in many different studies, the precise nature of how and why activity drifts is still an open question. Drift may be an inevitable outcome of normal protein and synapse turnover, where homeostatic processes maintain the core representation, similar to the famous ship of Theseus, in which all the components of the original ship are replaced over time. Alternatively, some argue that drift is a feature, not a bug, of computation. Memories require regular updating, and the continual reorganization of engrams or linking of memories across time may require some degree of drift. Experimental work that ties synaptic plasticity and engram formation with drift rates would potentially help reconcile these hypotheses.\r\n\r\nIn this essay, I aim to better understand how to unite these different models of memory. To do so, I asked researchers working at different levels of analysis the following three questions, to determine whether the extremes outlined here are, in fact, real or just perceived.\r\n

    \r\n \t
  1. Is information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/li>\r\n \t
  2. Can we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/li>\r\n \t
  3. What experimental data would be helpful to reconcile these observations to help bring these theories together?<\/li>\r\n<\/ol>"},{"acf_fc_layout":"section_title_comp","section_title":"Responses"},{"acf_fc_layout":"accordion","items":[{"title":"Andr\u00e9 Fenton","subtitle":"New York University; Contributing editor, The Transmitter<\/em>","body":"[tt_rounded_inline_image image_id='218714'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/strong>\r\n\r\nBoth. One problem is that we conceive of memory as something the external world imposes on the brain, but the reality is more bidirectional\u2014an interdependence exists between the bona fide stimuli and how the brain is organized to receive those stimuli. In many ways, the brain is projecting something akin to beliefs onto the stimuli, in order to process and store the experience. Another facet of this problem is the false dichotomy of the question. Information is the product of a dynamical process. It is not stored in any single element, and the storage is intimately related to the readout. Endel Tulving formalized this concept<\/a> with the term ecphory (originally from Richard Semon), meaning memory performance (mem) \u2248 I*C*M, where I is stored information, C is the cues that trigger the recall, and M is mindset.\r\n\r\nIn a dynamical system, it may not be practically possible to separate the process of storage from the access. For example, many of the same hippocampal cells seem to be engaged in both the encoding and recall of information in memory. Consider CA1 hippocampal place cells, which have at least two major anatomically distinct inputs, one from CA3 via the synapses at the Schaffer collateral terminals, and the other from entorhinal cortex (EC) via the synapses at the temporoammonic pathway terminals. The CA1 population discharge tends to represent the current location, as expected for place cells. But a study<\/a> my group published in 2018 showed that on the infrequent occasions that the CA3 inputs dominate the EC inputs, the CA1 population discharge represents locations remote from the current location, such as where the mouse recollects being shocked. (In the experiment, the mouse will actively avoid this place for the next two seconds.) The place information for the entire environment was presumably stored in a distributed set of CA1 synapses accessible via presynaptic activity that could originate from CA3 or from EC, each activating different subsets of CA1 synapses.\r\n\r\n[tt_sidebar_quote author='']To better understand neurobiological systems, the field has to learn to rigorously integrate across levels with both data collection and data analysis.[\/tt_sidebar_quote]\r\n\r\nThe fact that CA1 output represents remote or current places depending on the relative CA3 and EC input suggests both the synapses and the input activity contribute to the output representation. The information stored in synapses is analogous to how an artificial neural network stores information in a covariance matrix that describes the interactions among its elements. Activity of the network can generate a version of the covariance matrix to encode or read out information. The information being stored and the circuit that is operating on the information are strongly interdependent.\r\n\r\nWe all know that biological processes operate and interact simultaneously across different levels of biological organization. But because experiments that span many levels are hard (but not impossible) to do, we tend to dichotomize instead of integrate. To better understand neurobiological systems, the field has to learn to rigorously integrate across levels with both data collection and data analysis.\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/strong>\r\n\r\nYes. The population of synapses that defines a circuit can reliably generate a particular and reliable output pattern from variable inputs. Think of the population of 100 recorded cells as creating a state space where the activity of each cell is a dimension of the 100-dimensional state space. If the activity of some cells is correlated, then they don\u2019t signal information independently. Dimensionality reduction techniques, such as principal component analysis, can transform variable high-dimensional inputs into stable outputs by projecting them into one or more lower-dimension subspaces, where each subspace dimension is a composite of many correlated cells.\r\n\r\n \r\n\r\n[caption id=\"attachment_218932\" align=\"aligncenter\" width=\"1157\"]\"\"
    Shadow dance:<\/strong> Two distinct three-neuron patterns of activity can produce an identical 2D output if the activity is observed as a lower 2D projection onto an appropriately selected surface. Here the shadow cast by the 3D activity is analogous to the constant representational meaning of the variable activity, and the projection angle is analogous to the network of correlations (synaptic weights) through which the population activity passes.[\/caption]\r\n\r\n    This video<\/a> from a 2023 paper in Cell Reports <\/em>offers a more intuitive visualization of how different patterns of activity can produce the same meaning, so long as the patterns are similar in the relevant subspace. Conversely, similar population activity patterns can generate distinct representations if they pass through distinct synaptic networks, each emphasizing different correlation patterns and thus distinct subspaces. Furthermore, drift in a high-dimensional space need not produce a read-out problem, because the high-dimensional space is so large that two drifting population patterns will only rarely come close by chance. This is analogous to two families in one apartment building. The families remain distinct even if the individuals move about each apartment.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nSimultaneous neural ensemble activity recordings during the manipulations of so-called engram cell recordings will go a long way toward clarifying the apparent contradiction. We have made such recordings and have a preliminary report<\/a> on bioRxiv. (We are adding a place cell study to the work and will then send it out for review.) Researchers have generally assumed that in a dynamical system, such as the CA1 network, stimulated neurons will reliably respond to the stimulation and not to the dictates of the network. But we found this assumption is wrong\u2014the whole network of cells responds to the stimulation, whether or not specific cells are directly stimulated. This is similar to how collective behavior is organized in interacting populations of individuals, such as flocks of birds and schools of fish. (For more on this, see my review, \u201cRemapping revisited: How the hippocampus represents different spaces<\/a>,\u201d in Nature Reviews Neuroscience<\/em>.)"},{"title":"Loren Frank","subtitle":"University of California, San Francisco","body":"[tt_rounded_inline_image image_id='218781'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?\u00a0<\/strong>\r\n\r\nThe answer here is likely \u201cyes,\u201d in that cellular changes that enable memory storage are likely to occur not only at individual synapses but also at the level of individual neurons whose cell-biological features may change as memories are stored. As an example, it might be that changes in gene expression lead to changes in activity levels, although at the moment we really don\u2019t understand the scope of these changes.\u00a0<\/strong>\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?\u00a0<\/strong>\r\n\r\nThe answer here is also \u201cyes,\u201d but to get there we first need to step back and think about what it means to store and then retrieve a memory. Focusing on memories for the events of daily life, our current conception is that the events themselves drive activity across the brain, engaging specific neurons whose activity represents the various sights, sounds, smells and feelings that are part of the experience. These activity patterns converge on the hippocampus, a brain structure critical for the formation of memories of events, and rapid changes in synapses (and perhaps other cellular features) within the hippocampus modify hippocampal activity and create a new pattern associated with the event.\r\n\r\nThis first stage of memory formation must also be accompanied by a second stage in which the hippocampal pattern is itself associated with activity outside the hippocampus. This association, which presumably also involves synaptic plasticity, would enable the hippocampal pattern to \u201cretrieve\u201d the memory by reactivating neurons outside the hippocampus that encode aspects of the original experience. The key point here is that this loop of associations, from information processed outside the hippocampus to a hippocampal index and back again to the rest of the brain, is what makes memory formation and subsequent retrieval possible.\r\n\r\nAnd now we can return to the question. If the hippocampal and extra-hippocampal patterns evolve together and remain connected, retrieval remains possible. The details of what is retrieved might themselves evolve as the extra-hippocampal patterns change, a feature that might help us understand the malleability of memory over time.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nAt a circuit level, the key prediction of the framework described above is that drift occurs concurrently across regions to maintain the links that enable retrieval. Direct measurements of cross-regional drift would be critical here. Moreover, we\u2019d expect these changes to be happening most prominently in \u201coffline\u201d periods, including quiet wakefulness and sleep. If so, we need to make these measurements continuously over long time periods to be able to understand what is happening to these representations and when it happens."},{"title":"Kari Hoffman","subtitle":"Vanderbilt University","body":"[tt_rounded_inline_image image_id='218915'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/strong>\r\n\r\nYes. First, I agree with the premise: Information is stored in the brain at some level(s). It\u2019s worth noting that not everyone agrees with this. Information can be \u201cstored\u201d at multiple levels: synapses, cells, circuits, the body. It can even be fully externalized, back into the environment, as cues or changes we create, as complex as photos and writing and as simple as a reminder string around a finger. All can involve information in service of memory. I would submit that much of the heavy lifting is done at both the synaptic and circuit\/ensemble level. Which levels dominate depends on factors such as memory type, when information was acquired and how it is integrated with the existing structures, themselves reflecting changes from earlier experiences. Granted, our methods have been biased to evaluate changes at the synaptic level, or at the level of cellular changes as proxy for synaptic changes.\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these memory engram drifts?<\/strong>\r\n\r\nWhy not? Whoever said memory needs to be supported by a single mechanism or at a single level? That said, we may need to be careful in using the term \u201cthese memories\u201d or \u201cthese memory engrams.\u201d Such terms suggest that experience creates biological bins to hold discrete memories, that memories exist as entities that are created \u201cde novo,\u201d and that neural modifications must reside at only one level, all of which are positions that are not or may not be true.\r\n\r\nI don\u2019t think a coexistence is any more troublesome than other accounts of brain function that cross levels of scale. For example, we accept that synapses may exhibit a variety of short-term plasticity mechanisms such as facilitation and adaptation. This doesn\u2019t preclude long-term synaptic modifications. Rather, the two coexist. For example, changes in postsynaptic receptors alter the strength of the synapse over longer time frames. Although this effect could, in principle, eclipse any short-term modifications, it doesn\u2019t necessarily have to. The two processes could be superposed, whereby short-term effects could ride on top of the new baseline created by the latter.\r\n\r\nSimilarly, groups of cells may become newly recruited to become co-active, and some of the original \u201ccast members\u201d\u00a0rotated off, with the net effect of preserving a pattern that arose from previous experience. As this happens, the change would supersede the effects of strengthened synapses of the original cast members. So the notion of a fixed \u201cengram\u201d over time increasingly misses the true target.\r\n\r\nImportantly, certain kinds of systematic, structured change in the members of an assembly can nevertheless preserve the decodablility, or identity, of a pattern. This would allow the organism to respond adaptively, based on knowledge (synthesized memories) accrued over time. Furthermore, fluctuations among constituent members of the many patterns needed for structured memories could help reallocate those cells best suited physiologically to contribute to new patterns, by criteria such as current excitability, clustered synapses or homeostatic plasticity, for example.\r\n\r\nIn the extreme, this reallocation may even involve a gradual change in the dominant \u201cownership\u201d realized across whole brain regions. On one hand, this notion is highly speculative, but on the other hand, considering the long-standing evidence for memory-guided responses that arise from wholly separate brain regions and circuits over time, I would suggest the question is \u201chow\u201d and not \u201cwhether\u201d such dynamics are in place. The time is ripe for modeling the conditions necessary for maintaining patterns under conditions of drift, to balance increased capacity and generalization with energy efficiency and to use empirical investigation to inform the biological constraints that need to be in place.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nI\u2019m not sure the goal is to bring them together, as they may coexist (see above). I\u2019ll show my bias: We know substantially less at the ensemble level than at the IEG activity-tagged or synaptic plasticity level. Even the long-standing replay literature\u2014the OG, time-resolved \u201cengrams,\u201d if you like\u2014has evaluated cell assemblies using methods that work by assuming mostly stationary membership. To confidently reject ideas around the \u201censemble doctrine\u201d for memories requires measuring ensembles and constituent members\u2019 activities over time for multiple different remembered experiences.\r\n\r\nOne prediction from an evolutionary lens is that species that live longer and have larger, more complex cortices may benefit disproportionately from the allocation of patterns at the \u201cfluid ensemble\u201d level. One possibility is that phenomena associated with dynamic ensembles, such as \u201crepresentational drift,\u201d\u00a0have\u00a0conserved topological patterns that support long-term flexible memory performance. Whether that\u2019s the case remains to be seen.\r\n\r\nTL;DR\u2014One promising directive: Measure more manifolds of monkey memories."},{"title":"Yingxi Lin","subtitle":"University of Texas Southwestern Medical Center","body":"[tt_rounded_inline_image image_id='218924'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/strong>\r\n\r\nI consider that information is stored at the systems level, and that it consists of both assemblies of cells and the synapses that connect them. It is, however, too early to say that those cells and synapses are sites of stored memory per se, as they may simply function to gain access to the memory. In addition, one should consider the existence of functionally distinct active ensembles within the engram circuits. Finally, it is also possible that there aren\u2019t specific sites for memory storage; cells and synapses may be part of a brain-wide code for memory expression.\r\n\r\n[tt_sidebar_quote author='']It is perhaps easier to reconcile if we consider that the \u201cengram\u201d circuits identified by current technology are merely vessels to fetch memories.[\/tt_sidebar_quote]\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?\u00a0<\/strong>\r\n\r\nTo reconcile these observations, it is important to understand the biological meanings behind discrete engram circuits and their drifts. It is perhaps easier to reconcile if we consider that the \u201cengram\u201d circuits identified by current technology are merely vessels to fetch memories.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?\u00a0<\/strong>\r\n\r\nExperiments addressing why the \u201cengram\u201d neurons drift and how they drift are much needed. To start with, I would suggest thorough monitoring of the drifting \u201cengram\u201d neurons, coupled with careful examination of their synaptic connectivity."},{"title":"Cian O\u2019Donnell","subtitle":"Ulster University","body":"[tt_rounded_inline_image image_id='207889'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?\u00a0<\/strong>\r\n\r\nSynapses in circuits! The field has held synaptic plasticity up as the main mechanism for information storage in the brain for several decades now, and I haven\u2019t heard any good reasons to start doubting it yet.\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/strong>\r\n\r\n[tt_sidebar_quote author='']The field has held synaptic plasticity up as the main mechanism for information storage in the brain for several decades now, and I haven\u2019t heard any good reasons to start doubting it yet.[\/tt_sidebar_quote]\r\n\r\nYes. We know from the work of Eve Marder and others, and more recently from studying deep neural networks, that degeneracy is likely to be ubiquitous in the brain. Many, many different cellular configurations can achieve the same circuit function. If and how brains evolved to use this degeneracy to maintain long-term memories is unknown. But it seems both possible and extremely useful.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nThe dream experiment would be to track the strengths of many thousands of synapses and record the electrical activity and gene expression of their associated pre- and postsynaptic neurons, longitudinally in vivo, before and during encoding of a memory in a living animal and then again later during memory recall a few days or weeks later. This will, of course, need to be done in a brain circuit that we know stores an essential component of the engram underlying the initial memory encoding. These data would let us ask if synaptic changes correlate with neural activity changes, and in turn ask if these correlate with any changes in the behavioral expression of memory. As a humble theorist, I will naturally be leaving these straightforward experiments as an exercise for my wet-lab colleagues."},{"title":"Timothy O\u2019Leary","subtitle":"University of Cambridge","body":"[tt_rounded_inline_image image_id='218929'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/strong>\r\n\r\nBoth. For the brain to learn new information, there needs to be a history-dependent change in its internal properties that ultimately manifests in behavioral changes. This means that neural circuits alter their properties, and we know for sure that synaptic changes are involved in many situations. Even if all <\/em>the changes were concentrated at synapses (and we know that isn\u2019t always the case), the \u201cinformation\u201d is distributed because it can be accessed only through the aggregate dynamics of a circuit.\r\n\r\nWhy does this question persist? Perhaps due to some of the most influential studies in the field, most obviously Eric Kandel\u2019s work on the gill withdrawal reflex in Aplysia. This work is famous for identifying changes at a single synapse that underlie habituation\u2014i.e., a change in behavior. This finding was possible because it exploited an unusually narrow bottleneck in the architecture of a specific circuit. So, we must pay attention to the fact that what made this circuit experimentally tractable is the same thing that makes it an outlier among neural circuits generally. Therefore, although some features of this work generalize\u2014for example, some of the biochemistry involved\u2014we shouldn\u2019t expect such peculiar wiring in other circuits and species, and such a precise relationship between synaptic efficacy and behavior.\r\n\r\nThis is a paradox of \u201cmodel systems\u201d: By selecting models that permit manipulations that are impossible in general, we can end up with insights that can\u2019t possibly generalize.\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/strong>\r\n\r\nYes. Let\u2019s remind ourselves of the granularity in the evidence here. The classic engram experiments demonstrate broad correlations between behavior and reactivation of subpopulations of \u201cengram\u201d neurons that are tagged during some kind of conditioned learning. This could be freezing behavior, triggered by the association of a foot shock with a visual cue. Is there some degree of variability in the behavior during engram reactivation? Yes. Is there some degree of variability in the state of the network during engram reactivation? Undoubtedly. Do we believe that the \u201cmemory\u201d is recapitulated exactly as if the animal is remembering the experience? I don\u2019t think we do. For one thing, the means of reactivating engram cells produces activity patterns that the brain never generates endogenously. But as long as there is sufficient overlap in the stimulated population and any vestige of a \u201ctrue\u201d engram, the behavior can be biased toward the conditioned behavior.\r\n\r\n[tt_sidebar_quote author='']Drift happens. Not all circuits drift to the same extent, but even in those that do, there is overwhelming evidence that behaviorally relevant information is preserved over behaviorally relevant timescales.[\/tt_sidebar_quote]\r\n\r\nPeople may quibble about details here, but the point I think most will agree on is that identifying and manipulating engrams is a statistical, population-level kind of analysis\u2014and this is where drift comes in. At the population level, a circuit can drift significantly and still allow binary discrimination between encoded associations, for example. This is the fundamental fact that reconciles these observations, and it has been pointed out many times in the drift literature.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nWe already have them. In the drift literature, multiple analyses show that associations can be decoded far above chance in a drifting population, and even quantitative predictions of the animal\u2019s behavior, such as its running trajectory through a maze, can be decoded despite drift. And let\u2019s keep in mind that this decoding is done with a small subset of the neurons in any given circuit and with fairly crude statistical models. The brain can probably do better.\r\n\r\nMore data will help, of course. Personally, I would like to see much longer-term studies of neural representations in multiple neural circuits, so we can track engrams as they evolve and track the co-evolution of different circuits. This will let us tease out components that are related to behavioral change and understand how much the brain evolves in unison.\r\n\r\nSo, in summary: Drift happens. Not all circuits drift to the same extent, but even in those that do, there is overwhelming evidence that behaviorally relevant information is preserved over behaviorally relevant timescales. That\u2019s all that is required of the biology. What we expect to see in an experiment is another thing and is often as much a product of our textbook paradigms as it is a reflection of reality."},{"title":"Tom\u00e1s Ryan","subtitle":"Trinity College Dublin","body":"[tt_rounded_inline_image image_id='201192'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?<\/strong>\r\n\r\nIt depends on what we mean by \u201cinformation.\u201d In many discussions, \u201cinformation\u201d simply refers to plasticity or a correlation of neuronal activity with some stimulus presentation. In that sense, a memory could be said to be stored at multiple levels, but what we would really be saying is that biological activity at those levels enables memory function. However, when I am pursuing the question of \u201cinformation,\u201d I want to know the essential level at which information that an animal knows about the world is stored as an engram. It seems to me that the plausible level for the storage of long-term memories is in the topography of the connectome. So, the information is engraved through stable changes in the brain's microanatomical circuit. Although specific molecules, organelles, synapses and neurons are certainly all involved in the formation of such an engram, they may be dispensable for the retention and coding of the information itself.\r\n\r\n[tt_sidebar_quote author='']A clearer question might be to ask how a comprehensive engram theory can incorporate the facts of what we call \u2018drift.\u2019 How can this extend our understanding of engrams? And what predictions would such a theory make?[\/tt_sidebar_quote]\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/strong>\r\n\r\nI don't see these two things as conflicting observations. On the one hand, we have a theory of engrams that has been tested (and so validated) by behavioral experiments in which engram ensembles are manipulated. On the other, we have a set of observations of ensemble activity and flux over time, popularly referred to as \u201crepresentational drift.\u201d A clearer question might be to ask how a comprehensive engram theory can incorporate the facts of what we call \u201cdrift.\u201d How can this extend our understanding of engrams? And what predictions would such a theory make?\u00a0<\/strong>\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nThe obvious question is, \u201cWhere do these (drifting) representations come from?\u201d And, \u201cWhat happened to the original representation?\u201d We already know, from engram experiments, that the original representation is still present and functional in the brain, but inactive. Currently, we know less about the origin of the drifting representations. Were they there at the time of learning, but inactive? Do they represent degeneracy or redundancy in how engrams are formed? Or are they simply newer engrams that were learned as the animal has progressed through life? In a sense, representational drift may simply be a name we give the phenomenology of different engrams competing for expression in the brain."},{"title":"Evan Schaffer","subtitle":"Icahn School of Medicine at Mount Sinai","body":"[tt_rounded_inline_image image_id='218931'][\/tt_rounded_inline_image]\r\n\r\nIs information stored in the brain at the level of cells (or circuits) or at the level of synapses?\u00a0<\/strong>\r\n\r\nI actually slightly disagree with the premise of the question\u2014I\u2019m convinced by some of the evidence on all of these scales, so I don\u2019t see the question so much as identifying which scale is correct, as I see it as trying to understand how the evidence on these scales fits together.\u00a0I find the evidence pretty convincing that activity in many parts of the brain, including canonical learning and memory centers such as the hippocampus, change over time. As a consequence, I don\u2019t think information can be stored in cells or synapses in the hippocampus in a way that is stable over a lifetime. In other parts of the brain, this may not be the case. But I think that for these circuits that drift, we\u2019re forced to think about how the brain can remember things despite the lack of permanent engrams. To me, this comes down to a question about compensatory learning rules. If the brain is constantly updating memories to account for drift, that could be a more robust solution that allows for ongoing learning without corrupting old memories.\u00a0<\/strong>\r\n\r\n[tt_sidebar_quote author='']I think it\u2019s possible that the apparent conflict between observations of engrams and drift might be due to issues of scale and subtleties in experimental design.[\/tt_sidebar_quote]\r\n\r\nCan we reconcile observations that show distinct engram circuits seem to store memories versus observations that show the neuronal activity of these engram neurons drifts?<\/strong>\r\n\r\nI think it\u2019s possible that the apparent conflict between observations of engrams and drift might be due to issues of scale and subtleties in experimental design. On a timescale of a few days, memories seem pretty stable. On a timescale of a few weeks, there\u2019s less evidence for stability.\u00a0The degree of stability also seems to depend on factors such as how many times the animal experiences the same stimulus, so in that sense I think the apparent conflict may also depend in part on experimental design.\r\n\r\nWhat experimental data would be helpful to reconcile these observations to help bring these theories together?<\/strong>\r\n\r\nI think standardizing experimental paradigms across labs that are seeing different things would be helpful. If the discrepancies are really an issue of timescale or the details of experimental design, having two labs agree on the details and run a side-by-side comparison could help. Even if we were to find that evidence for stability or drift depends on the details, that alone wouldn\u2019t bring the theories together, but it would provide useful constraints for computational work to try to unify the theories."}]},{"acf_fc_layout":"section_title_comp","section_title":"Conclusions"},{"acf_fc_layout":"copy_comp","copy":"In posing these questions and setting up the paradox of stability versus drift, I purposely chose extreme views to stimulate discussion. But none of our memory experts believe it is one or the other. This is clear in the responses we received, which predominately took the position that memories are stored across synapses, cells AND circuits. Indeed, the major hurdle in understanding how memories are formed and consolidated is the challenge of gathering data at all the different scales of analysis open to modern neuroscience\u2014from gene expression to behavior. For example, the ability to label memory engram cells still relies on rather crude and artificial means, using immediate early gene promoters.\r\n\r\nLabeling and identifying a \u201csynaptic engram\u201d may reveal more fine-grained dynamics of how circuits evolve over time to represent experiences. All our experts believe that drift is a real phenomenon, but their views diverge on the function of drift and whether drift represents degeneracy or redundancy.\r\n\r\nThe \u201cstatic\u201d view of synaptic plasticity that emerged from studies in simple circuits, such as in Aplysia, has largely been replaced by more refined ideas that incorporate a systems neuroscience perspective. In reconciling a static synaptic plasticity or connectome hypothesis, where information is ultimately stored as a permanent \u201cmap,\u201d population-level dynamics of neurons in a circuit and the evolution of brain representations have to be considered.\r\n\r\nFinally, neuroscientists must do a better job of defining their terms. What is \u201cinformation,\u201d and how is it \u201crepresented\u201d in the brain? What is an engram? This is perhaps where the most confusion occurs, because these terms mean different things depending on the level of analysis used. The best way to move the field forward is to create more opportunities for experts to come together and discuss studies that span many levels of analysis and understanding."},{"acf_fc_layout":"newsletter","title":"Subscribe to get notified every time a new \u201cThe big picture\u201d is published.","subtitle":" Researchers ask colleagues to weigh in on important topics in the field.","bg_image":200913,"groups":[{"group":"19","name":"","hide_checkbox":true}],"linktext":"","linkurl":""}]},"_links":{"self":[{"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/posts\/218904"}],"collection":[{"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/comments?post=218904"}],"version-history":[{"count":18,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/posts\/218904\/revisions"}],"predecessor-version":[{"id":219038,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/posts\/218904\/revisions\/219038"}],"acf:post":[{"embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/contributor\/107456"},{"embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/contributor\/211270"}],"acf:term":[{"embeddable":true,"taxonomy":"post_tag","href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/tags\/319"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/media\/218920"}],"wp:attachment":[{"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/media?parent=218904"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/categories?post=218904"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.thetransmitter.org\/wp-json\/wp\/v2\/tags?post=218904"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}