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Lewis CM, Hoffmann A, Helmchen F. Linking brain activity across scales with simultaneous opto- and electrophysiology. NEUROPHOTONICS 2024; 11:033403. [PMID: 37662552 PMCID: PMC10472193 DOI: 10.1117/1.nph.11.3.033403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023]
Abstract
The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.
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Affiliation(s)
| | - Adrian Hoffmann
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
- University of Zurich, University Research Priority Program, Adaptive Brain Circuits in Development and Learning, Zurich, Switzerland
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2
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Dobler Z, Suresh A, Chari T, Mula S, Tran A, Buonomano DV, Portera-Cailliau C. Adapting and facilitating responses in mouse somatosensory cortex are dynamic and shaped by experience. Curr Biol 2024:S0960-9822(24)00854-6. [PMID: 39059392 DOI: 10.1016/j.cub.2024.06.070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/10/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024]
Abstract
Sensory adaptation is the process whereby brain circuits adjust neuronal activity in response to redundant sensory stimuli. Although sensory adaptation has been extensively studied for individual neurons on timescales of tens of milliseconds to a few seconds, little is known about it over longer timescales or at the population level. We investigated population-level adaptation in the barrel field of the mouse somatosensory cortex (S1BF) using in vivo two-photon calcium imaging and Neuropixels recordings in awake mice. Among stimulus-responsive neurons, we found both adapting and facilitating neurons, which decreased or increased their firing, respectively, with repetitive whisker stimulation. The former outnumbered the latter by 2:1 in layers 2/3 and 4; hence, the overall population response of mouse S1BF was slightly adapting. We also discovered that population adaptation to one stimulus frequency (5 Hz) does not necessarily generalize to a different frequency (12.5 Hz). Moreover, responses of individual neurons to repeated rounds of stimulation over tens of minutes were strikingly heterogeneous and stochastic, such that their adapting or facilitating response profiles were not stable across time. Such representational drift was particularly striking when recording longitudinally across 8-9 days, as adaptation profiles of most whisker-responsive neurons changed drastically from one day to the next. Remarkably, repeated exposure to a familiar stimulus paradoxically shifted the population away from strong adaptation and toward facilitation. Thus, the adapting vs. facilitating response profile of S1BF neurons is not a fixed property of neurons but rather a highly dynamic feature that is shaped by sensory experience across days.
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Affiliation(s)
- Zoë Dobler
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA; Neuroscience Interdepartmental Program, University of California, Los Angeles, 695 Charles Young Drive South, Los Angeles, CA 90095, USA
| | - Anand Suresh
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA
| | - Trishala Chari
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA; Neuroscience Interdepartmental Program, University of California, Los Angeles, 695 Charles Young Drive South, Los Angeles, CA 90095, USA
| | - Supriya Mula
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA
| | - Anne Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA
| | - Dean V Buonomano
- Department of Neurobiology, David Geffen School of Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA; Department of Psychology, University of California, Los Angeles, 502 Portola Plaza, Los Angeles, CA 90095, USA
| | - Carlos Portera-Cailliau
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, USA; Department of Neurobiology, David Geffen School of Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA.
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3
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Wirt RA, Soluoku TK, Ricci RM, Seamans JK, Hyman JM. Temporal information in the anterior cingulate cortex relates to accumulated experiences. Curr Biol 2024; 34:2921-2931.e3. [PMID: 38908372 DOI: 10.1016/j.cub.2024.05.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 04/02/2024] [Accepted: 05/23/2024] [Indexed: 06/24/2024]
Abstract
Anterior cingulate cortex (ACC) activity is important for operations that require the ability to integrate multiple experiences over time, such as rule learning, cognitive flexibility, working memory, and long-term memory recall. To shed light on this, we analyzed neuronal activity while rats repeated the same behaviors during hour-long sessions to investigate how activity changed over time. We recorded neuronal ensembles as rats performed a decision-free operant task with varying reward likelihoods at three different response ports (n = 5). Neuronal state space analysis revealed that each repetition of a behavior was distinct, with more recent behaviors more similar than those further apart in time. ACC activity was dominated by a slow, gradual change in low-dimensional representations of neural state space aligning with the pace of behavior. Temporal progression, or drift, was apparent on the top principal component for every session and was driven by the accumulation of experiences and not an internal clock. Notably, these signals were consistent across subjects, allowing us to accurately predict trial numbers based on a model trained on data from a different animal. We observed that non-continuous ramping firing rates over extended durations (tens of minutes) drove the low-dimensional ensemble representations. 40% of ACC neurons' firing ramped over a range of trial lengths and combinations of shorter duration ramping neurons created ensembles that tracked longer durations. These findings provide valuable insights into how the ACC, at an ensemble level, conveys temporal information by reflecting the accumulation of experiences over extended periods.
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Affiliation(s)
- Ryan A Wirt
- University of Nevada, Las Vegas, Interdisciplinary Program in Neuroscience, Las Vegas, NV 89154-1003, USA
| | - Talha K Soluoku
- University of Nevada, Las Vegas, Interdisciplinary Program in Neuroscience, Las Vegas, NV 89154-1003, USA
| | - Ryan M Ricci
- University of Nevada, Las Vegas, College of Medical Sciences, Las Vegas, NV 89154-1003, USA
| | - Jeremy K Seamans
- University of British Columbia, Department of Psychiatry, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada
| | - James M Hyman
- University of Nevada, Las Vegas, Interdisciplinary Program in Neuroscience, Las Vegas, NV 89154-1003, USA; University of Nevada, Las Vegas, Department of Psychology, Las Vegas, NV 89154-1003, USA.
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4
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Moore JJ, Genkin A, Tournoy M, Pughe-Sanford JL, de Ruyter van Steveninck RR, Chklovskii DB. The neuron as a direct data-driven controller. Proc Natl Acad Sci U S A 2024; 121:e2311893121. [PMID: 38913890 PMCID: PMC11228465 DOI: 10.1073/pnas.2311893121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 04/12/2024] [Indexed: 06/26/2024] Open
Abstract
In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.
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Affiliation(s)
- Jason J Moore
- Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | - Alexander Genkin
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | - Magnus Tournoy
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | | | | | - Dmitri B Chklovskii
- Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
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5
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Li Q, Sorscher B, Sompolinsky H. Representations and generalization in artificial and brain neural networks. Proc Natl Acad Sci U S A 2024; 121:e2311805121. [PMID: 38913896 PMCID: PMC11228472 DOI: 10.1073/pnas.2311805121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024] Open
Abstract
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribution and out-of-distribution contexts. We introduce two hypotheses: First, the geometric properties of the neural manifolds associated with discrete cognitive entities, such as objects, words, and concepts, are powerful order parameters. They link the neural substrate to the generalization capabilities and provide a unified methodology bridging gaps between neuroscience, machine learning, and cognitive science. We overview recent progress in studying the geometry of neural manifolds, particularly in visual object recognition, and discuss theories connecting manifold dimension and radius to generalization capacity. Second, we suggest that the theory of learning in wide DNNs, especially in the thermodynamic limit, provides mechanistic insights into the learning processes generating desired neural representational geometries and generalization. This includes the role of weight norm regularization, network architecture, and hyper-parameters. We will explore recent advances in this theory and ongoing challenges. We also discuss the dynamics of learning and its relevance to the issue of representational drift in the brain.
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Affiliation(s)
- Qianyi Li
- The Harvard Biophysics Graduate Program, Harvard University, Cambridge, MA02138
- Center for Brain Science, Harvard University, Cambridge, MA02138
| | - Ben Sorscher
- The Applied Physics Department, Stanford University, Stanford, CA94305
| | - Haim Sompolinsky
- Center for Brain Science, Harvard University, Cambridge, MA02138
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem9190401, Israel
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6
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Yang X, La Camera G. Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits. PLoS Comput Biol 2024; 20:e1012220. [PMID: 38950068 PMCID: PMC11244818 DOI: 10.1371/journal.pcbi.1012220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 07/12/2024] [Accepted: 06/01/2024] [Indexed: 07/03/2024] Open
Abstract
Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.
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Affiliation(s)
- Xiaoyu Yang
- Graduate Program in Physics and Astronomy, Stony Brook University, Stony Brook, New York, United States of America
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
- Center for Neural Circuit Dynamics, Stony Brook University, Stony Brook, New York, United States of America
| | - Giancarlo La Camera
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
- Center for Neural Circuit Dynamics, Stony Brook University, Stony Brook, New York, United States of America
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7
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Hira R. Closed-loop experiments and brain machine interfaces with multiphoton microscopy. NEUROPHOTONICS 2024; 11:033405. [PMID: 38375331 PMCID: PMC10876015 DOI: 10.1117/1.nph.11.3.033405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy-the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo-works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.
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Affiliation(s)
- Riichiro Hira
- Tokyo Medical and Dental University, Graduate School of Medical and Dental Sciences, Department of Physiology and Cell Biology, Tokyo, Japan
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8
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Frankland PW, Josselyn SA, Köhler S. Engrams. Curr Biol 2024; 34:R559-R561. [PMID: 38889673 DOI: 10.1016/j.cub.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Frankland et al. provide a history of research on engrams and their relationship to memory processes, highlighting new technologies that have allowed careful dissection of engrams and their function.
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Affiliation(s)
- Paul W Frankland
- Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Child and Brain Development Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Sheena A Josselyn
- Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Stefan Köhler
- Department of Psychology, University of Western Ontario, London, ON, Canada
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9
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Yang X, La Camera G. Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570692. [PMID: 38106233 PMCID: PMC10723399 DOI: 10.1101/2023.12.07.570692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.
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Affiliation(s)
- Xiaoyu Yang
- Graduate Program in Physics and Astronomy, Stony Brook University
- Department of Neurobiology & Behavior, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
| | - Giancarlo La Camera
- Department of Neurobiology & Behavior, Stony Brook University
- Center for Neural Circuit Dynamics, Stony Brook University
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10
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Kim JH, Daie K, Li N. A combinatorial neural code for long-term motor memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597627. [PMID: 38895416 PMCID: PMC11185691 DOI: 10.1101/2024.06.05.597627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.
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11
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Pellegrino A, Stein H, Cayco-Gajic NA. Dimensionality reduction beyond neural subspaces with slice tensor component analysis. Nat Neurosci 2024; 27:1199-1210. [PMID: 38710876 DOI: 10.1038/s41593-024-01626-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/20/2024] [Indexed: 05/08/2024]
Abstract
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
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Affiliation(s)
- Arthur Pellegrino
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - N Alex Cayco-Gajic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
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12
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Chen HT, van der Meer MAA. Paradoxical replay can protect contextual task representations from destructive interference when experience is unbalanced. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.09.593332. [PMID: 38766204 PMCID: PMC11100794 DOI: 10.1101/2024.05.09.593332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Experience replay is a powerful mechanism to learn efficiently from limited experience. Despite several decades of compelling experimental results, the factors that determine which experiences are selected for replay remain unclear. A particular challenge for current theories is that on tasks that feature unbalanced experience, rats paradoxically replay the less-experienced trajectory. To understand why, we simulated a feedforward neural network with two regimes: rich learning (structured representations tailored to task demands) and lazy learning (unstructured, task-agnostic representations). Rich, but not lazy, representations degraded following unbalanced experience, an effect that could be reversed with paradoxical replay. To test if this computational principle can account for the experimental data, we examined the relationship between paradoxical replay and learned task representations in the rat hippocampus. Strikingly, we found a strong association between the richness of learned task representations and the paradoxicality of replay. Taken together, these results suggest that paradoxical replay specifically serves to protect rich representations from the destructive effects of unbalanced experience, and more generally demonstrate a novel interaction between the nature of task representations and the function of replay in artificial and biological systems.
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Affiliation(s)
- Hung-Tu Chen
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH 03755
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13
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Ratzon A, Derdikman D, Barak O. Representational drift as a result of implicit regularization. eLife 2024; 12:RP90069. [PMID: 38695551 PMCID: PMC11065423 DOI: 10.7554/elife.90069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024] Open
Abstract
Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.
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Affiliation(s)
- Aviv Ratzon
- Rappaport Faculty of Medicine, Technion - Israel Institute of TechnologyHaifaIsrael
- Network Biology Research Laboratory, Technion - Israel Institute of TechnologyHaifaIsrael
| | - Dori Derdikman
- Rappaport Faculty of Medicine, Technion - Israel Institute of TechnologyHaifaIsrael
| | - Omri Barak
- Rappaport Faculty of Medicine, Technion - Israel Institute of TechnologyHaifaIsrael
- Network Biology Research Laboratory, Technion - Israel Institute of TechnologyHaifaIsrael
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14
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Bellafard A, Namvar G, Kao JC, Vaziri A, Golshani P. Volatile working memory representations crystallize with practice. Nature 2024; 629:1109-1117. [PMID: 38750359 PMCID: PMC11136659 DOI: 10.1038/s41586-024-07425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/15/2024] [Indexed: 05/31/2024]
Abstract
Working memory, the process through which information is transiently maintained and manipulated over a brief period, is essential for most cognitive functions1-4. However, the mechanisms underlying the generation and evolution of working-memory neuronal representations at the population level over long timescales remain unclear. Here, to identify these mechanisms, we trained head-fixed mice to perform an olfactory delayed-association task in which the mice made decisions depending on the sequential identity of two odours separated by a 5 s delay. Optogenetic inhibition of secondary motor neurons during the late-delay and choice epochs strongly impaired the task performance of the mice. Mesoscopic calcium imaging of large neuronal populations of the secondary motor cortex (M2), retrosplenial cortex (RSA) and primary motor cortex (M1) showed that many late-delay-epoch-selective neurons emerged in M2 as the mice learned the task. Working-memory late-delay decoding accuracy substantially improved in the M2, but not in the M1 or RSA, as the mice became experts. During the early expert phase, working-memory representations during the late-delay epoch drifted across days, while the stimulus and choice representations stabilized. In contrast to single-plane layer 2/3 (L2/3) imaging, simultaneous volumetric calcium imaging of up to 73,307 M2 neurons, which included superficial L5 neurons, also revealed stabilization of late-delay working-memory representations with continued practice. Thus, delay- and choice-related activities that are essential for working-memory performance drift during learning and stabilize only after several days of expert performance.
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Affiliation(s)
- Arash Bellafard
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ghazal Namvar
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, Henry Samueli School of Engineering, University of California, Los Angeles, CA, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Greater Los Angeles VA Medical Center, Los Angeles, CA, USA.
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Integrative Center for Learning and Memory, University of California, Los Angeles, CA, USA.
- Intellectual and Developmental Disability Research Center, University of California, Los Angeles, CA, USA.
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15
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Wei 魏赣超 G, Tajik Mansouri زینب تاجیک منصوری Z, Wang 王晓婧 X, Stevenson IH. Calibrating Bayesian Decoders of Neural Spiking Activity. J Neurosci 2024; 44:e2158232024. [PMID: 38538143 PMCID: PMC11063820 DOI: 10.1523/jneurosci.2158-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 05/03/2024] Open
Abstract
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.
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Affiliation(s)
- Ganchao Wei 魏赣超
- Department of Statistical Science, Duke University, Durham, North Carolina 27708
| | | | | | - Ian H Stevenson
- Departments of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
- Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Connecticut Institute for Brain and Cognitive Science, University of Connecticut, Storrs, Connecticut 06269
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16
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Losey DM, Hennig JA, Oby ER, Golub MD, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Yu BM, Chase SM. Learning leaves a memory trace in motor cortex. Curr Biol 2024; 34:1519-1531.e4. [PMID: 38531360 PMCID: PMC11097210 DOI: 10.1016/j.cub.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 12/06/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.
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Affiliation(s)
- Darby M Losey
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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17
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Tlaie A, Shapcott K, van der Plas TL, Rowland J, Lees R, Keeling J, Packer A, Tiesinga P, Schölvinck ML, Havenith MN. What does the mean mean? A simple test for neuroscience. PLoS Comput Biol 2024; 20:e1012000. [PMID: 38640119 PMCID: PMC11062559 DOI: 10.1371/journal.pcbi.1012000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 05/01/2024] [Accepted: 03/12/2024] [Indexed: 04/21/2024] Open
Abstract
Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.
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Affiliation(s)
- Alejandro Tlaie
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | | | - Thijs L. van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - James Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Robert Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Adam Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Paul Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Martha N. Havenith
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
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18
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Noda T, Aschauer DF, Chambers AR, Seiler JPH, Rumpel S. Representational maps in the brain: concepts, approaches, and applications. Front Cell Neurosci 2024; 18:1366200. [PMID: 38584779 PMCID: PMC10995314 DOI: 10.3389/fncel.2024.1366200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.
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Affiliation(s)
- Takahiro Noda
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Dominik F. Aschauer
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Anna R. Chambers
- Department of Otolaryngology – Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
- Eaton Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston, MA, United States
| | - Johannes P.-H. Seiler
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Simon Rumpel
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
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19
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Yuste R, Cossart R, Yaksi E. Neuronal ensembles: Building blocks of neural circuits. Neuron 2024; 112:875-892. [PMID: 38262413 PMCID: PMC10957317 DOI: 10.1016/j.neuron.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/07/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.
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Affiliation(s)
- Rafael Yuste
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA.
| | - Rosa Cossart
- Inserm, INMED, Turing Center for Living Systems Aix-Marseille University, Marseille, France.
| | - Emre Yaksi
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway; Koç University Research Center for Translational Medicine, Koç University School of Medicine, Istanbul, Turkey.
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20
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Piantadosi SC, Zhou ZC, Pizzano C, Pedersen CE, Nguyen TK, Thai S, Stuber GD, Bruchas MR. Holographic stimulation of opposing amygdala ensembles bidirectionally modulates valence-specific behavior via mutual inhibition. Neuron 2024; 112:593-610.e5. [PMID: 38086375 PMCID: PMC10984369 DOI: 10.1016/j.neuron.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 10/04/2023] [Accepted: 11/09/2023] [Indexed: 02/24/2024]
Abstract
The basolateral amygdala (BLA) is an evolutionarily conserved brain region, well known for valence processing. Despite this central role, the relationship between activity of BLA neuronal ensembles in response to appetitive and aversive stimuli and the subsequent expression of valence-specific behavior has remained elusive. Here, we leverage two-photon calcium imaging combined with single-cell holographic photostimulation through an endoscopic lens to demonstrate a direct causal role for opposing ensembles of BLA neurons in the control of oppositely valenced behavior in mice. We report that targeted photostimulation of either appetitive or aversive BLA ensembles results in mutual inhibition and shifts behavioral responses to promote consumption of an aversive tastant or reduce consumption of an appetitive tastant, respectively. Here, we identify that neuronal encoding of valence in the BLA is graded and relies on the relative proportion of individual BLA neurons recruited in a stable appetitive or quinine ensemble.
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Affiliation(s)
- Sean C Piantadosi
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Zhe Charles Zhou
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Carina Pizzano
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Christian E Pedersen
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Tammy K Nguyen
- Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Sarah Thai
- Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Garret D Stuber
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA; Department of Pharmacology, University of Washington, Seattle, WA, USA
| | - Michael R Bruchas
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA; Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA; Department of Pharmacology, University of Washington, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA.
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21
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Ratzon A, Derdikman D, Barak O. Representational drift as a result of implicit regularization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.04.539512. [PMID: 38370656 PMCID: PMC10871206 DOI: 10.1101/2023.05.04.539512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.
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Affiliation(s)
- Aviv Ratzon
- Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
- Network Biology Research Laboratory, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Dori Derdikman
- Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
| | - Omri Barak
- Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
- Network Biology Research Laboratory, Technion - Israel Institute of Technology, Haifa 32000, Israel
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22
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Wang C, Lee H, Rao G, Knierim JJ. Multiplexing of temporal and spatial information in the lateral entorhinal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578307. [PMID: 38352543 PMCID: PMC10862918 DOI: 10.1101/2024.01.31.578307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Episodic memory involves the processing of spatial and temporal aspects of personal experiences. The lateral entorhinal cortex (LEC) plays an essential role in subserving memory. However, the specific mechanism by which LEC integrates spatial and temporal information remains elusive. Here, we recorded LEC neurons while rats performed foraging and shuttling behaviors on one-dimensional, linear or circular tracks. Unlike open-field foraging tasks, many LEC cells displayed spatial firing fields in these tasks and demonstrated selectivity for traveling directions. Furthermore, some LEC neurons displayed changes in the firing rates of their spatial rate maps during a session, a phenomenon referred to as rate remapping. Importantly, this temporal modulation was consistent across sessions, even when the spatial environment was altered. Notably, the strength of temporal modulation was found to be greater in LEC compared to other brain regions, such as the medial entorhinal cortex (MEC), CA1, and CA3. Thus, the spatial rate mapping observed in LEC neurons may serve as a coding mechanism for temporal context, allowing for flexible multiplexing of spatial and temporal information.
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Affiliation(s)
- Cheng Wang
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD
| | - Heekyung Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD
| | - Geeta Rao
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD
| | - James J Knierim
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD
- Lead contact
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23
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Margolles P, Elosegi P, Mei N, Soto D. Unconscious Manipulation of Conceptual Representations with Decoded Neurofeedback Impacts Search Behavior. J Neurosci 2024; 44:e1235232023. [PMID: 37985180 PMCID: PMC10866193 DOI: 10.1523/jneurosci.1235-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/04/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback, we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioral significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioral consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift.
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Affiliation(s)
- Pedro Margolles
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia 48940, Spain
| | - Patxi Elosegi
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia 48940, Spain
| | - Ning Mei
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
| | - David Soto
- Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Gipuzkoa 20009, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Bizkaia 48009, Spain
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24
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O'Sullivan FM, Ryan TJ. If Engrams Are the Answer, What Is the Question? ADVANCES IN NEUROBIOLOGY 2024; 38:273-302. [PMID: 39008021 DOI: 10.1007/978-3-031-62983-9_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Engram labelling and manipulation methodologies are now a staple of contemporary neuroscientific practice, giving the impression that the physical basis of engrams has been discovered. Despite enormous progress, engrams have not been clearly identified, and it is unclear what they should look like. There is an epistemic bias in engram neuroscience toward characterizing biological changes while neglecting the development of theory. However, the tools of engram biology are exciting precisely because they are not just an incremental step forward in understanding the mechanisms of plasticity and learning but because they can be leveraged to inform theory on one of the fundamental mysteries in neuroscience-how and in what format the brain stores information. We do not propose such a theory here, as we first require an appreciation for what is lacking. We outline a selection of issues in four sections from theoretical biology and philosophy that engram biology and systems neuroscience generally should engage with in order to construct useful future theoretical frameworks. Specifically, what is it that engrams are supposed to explain? How do the different building blocks of the brain-wide engram come together? What exactly are these component parts? And what information do they carry, if they carry anything at all? Asking these questions is not purely the privilege of philosophy but a key to informing scientific hypotheses that make the most of the experimental tools at our disposal. The risk for not engaging with these issues is high. Without a theory of what engrams are, what they do, and the wider computational processes they fit into, we may never know when they have been found.
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Affiliation(s)
- Fionn M O'Sullivan
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Tomás J Ryan
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, University of Melbourne, Melbourne, VIC, Australia.
- Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada.
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25
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Krishnan S, Sheffield ME. Reward Expectation Reduces Representational Drift in the Hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.21.572809. [PMID: 38187677 PMCID: PMC10769341 DOI: 10.1101/2023.12.21.572809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Spatial memory in the hippocampus involves dynamic neural patterns that change over days, termed representational drift. While drift may aid memory updating, excessive drift could impede retrieval. Memory retrieval is influenced by reward expectation during encoding, so we hypothesized that diminished reward expectation would exacerbate representational drift. We found that high reward expectation limited drift, with CA1 representations on one day gradually re-emerging over successive trials the following day. Conversely, the absence of reward expectation resulted in increased drift, as the gradual re-emergence of the previous day's representation did not occur. At the single cell level, lowering reward expectation caused an immediate increase in the proportion of place-fields with low trial-to-trial reliability. These place fields were less likely to be reinstated the following day, underlying increased drift in this condition. In conclusion, heightened reward expectation improves memory encoding and retrieval by maintaining reliable place fields that are gradually reinstated across days, thereby minimizing representational drift.
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26
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McFadden J. Carving Nature at Its Joints: A Comparison of CEMI Field Theory with Integrated Information Theory and Global Workspace Theory. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1635. [PMID: 38136515 PMCID: PMC10743215 DOI: 10.3390/e25121635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
The quest to comprehend the nature of consciousness has spurred the development of many theories that seek to explain its underlying mechanisms and account for its neural correlates. In this paper, I compare my own conscious electromagnetic information field (cemi field) theory with integrated information theory (IIT) and global workspace theory (GWT) for their ability to 'carve nature at its joints' in the sense of predicting the entities, structures, states and dynamics that are conventionally recognized as being conscious or nonconscious. I go on to argue that, though the cemi field theory shares features of both integrated information theory and global workspace theory, it is more successful at carving nature at its conventionally accepted joints between conscious and nonconscious systems, and is thereby a more successful theory of consciousness.
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Affiliation(s)
- Johnjoe McFadden
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
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27
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Rabus A, Curic D, Ivan VE, Esteves IM, Gruber AJ, Davidsen J. Changes in functional connectivity preserve scale-free neuronal and behavioral dynamics. Phys Rev E 2023; 108:L052301. [PMID: 38115411 DOI: 10.1103/physreve.108.l052301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/06/2023] [Indexed: 12/21/2023]
Abstract
Does the brain optimize itself for storage and transmission of information, and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of subpopulations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.
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Affiliation(s)
- Anja Rabus
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Davor Curic
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Victorita E Ivan
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Ingrid M Esteves
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Aaron J Gruber
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Jörn Davidsen
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada T2N 4N1
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28
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Levy ERJ, Carrillo-Segura S, Park EH, Redman WT, Hurtado JR, Chung S, Fenton AA. A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields. Cell Rep 2023; 42:113142. [PMID: 37742193 PMCID: PMC10842170 DOI: 10.1016/j.celrep.2023.113142] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/14/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.
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Affiliation(s)
| | - Simón Carrillo-Segura
- Center for Neural Science, New York University, New York, NY 10003, USA; Graduate Program in Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
| | - Eun Hye Park
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - William Thomas Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY 10003, USA; Flatiron Institute Center for Computational Neuroscience, New York, NY 10010, USA
| | - André Antonio Fenton
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute at the NYU Langone Medical Center, New York, NY 10016, USA.
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29
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Alisha A, Bettina V, Simon P. Representational drift in barrel cortex is receptive field dependent. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563381. [PMID: 37961727 PMCID: PMC10634719 DOI: 10.1101/2023.10.20.563381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cortical populations often exhibit changes in activity even when behavior is stable. How behavioral stability is maintained in the face of such 'representational drift' remains unclear. One possibility is that some neurons are stable despite broader instability. We examine whisker touch responses in superficial layers of primary vibrissal somatosensory cortex (vS1) over several weeks in mice stably performing an object detection task with two whiskers. While the number of touch neurons remained constant, individual neurons changed with time. Touch-responsive neurons with broad receptive fields were more stable than narrowly tuned neurons. Transitions between functional types were non-random: before becoming broadly tuned neurons, unresponsive neurons first pass through a period of narrower tuning. Broadly tuned neurons with higher pairwise correlations to other touch neurons were more stable than neurons with lower correlations. Thus, a small population of broadly tuned and synchronously active touch neurons exhibit elevated stability and may be particularly important for downstream readout.
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Affiliation(s)
- Ahmed Alisha
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003
| | - Voelcker Bettina
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003
| | - Peron Simon
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003
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30
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Srinivasan S, Daste S, Modi MN, Turner GC, Fleischmann A, Navlakha S. Effects of stochastic coding on olfactory discrimination in flies and mice. PLoS Biol 2023; 21:e3002206. [PMID: 37906721 PMCID: PMC10618007 DOI: 10.1371/journal.pbio.3002206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/21/2023] [Indexed: 11/02/2023] Open
Abstract
Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.
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Affiliation(s)
- Shyam Srinivasan
- Kavli Institute for Brain and Mind, University of California, San Diego, California, United States of America
- Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Simon Daste
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island, United States of America
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
| | - Mehrab N. Modi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Glenn C. Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Alexander Fleischmann
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island, United States of America
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
| | - Saket Navlakha
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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31
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Barwich AS, Severino GJ. The Wire Is Not the Territory: Understanding Representational Drift in Olfaction With Dynamical Systems Theory. Top Cogn Sci 2023. [PMID: 37690113 DOI: 10.1111/tops.12689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Representational drift is a phenomenon of increasing interest in the cognitive and neural sciences. While investigations are ongoing for other sensory cortices, recent research has demonstrated the pervasiveness in which it occurs in the piriform cortex for olfaction. This gradual weakening and shifting of stimulus-responsive cells has critical implications for sensory stimulus-response models and perceptual decision-making. While representational drift may complicate traditional sensory processing models, it could be seen as an advantage in olfaction, as animals live in environments with constantly changing and unpredictable chemical information. Non-topographical encoding in the olfactory system may aid in contextualizing reactions to promiscuous odor stimuli, facilitating adaptive animal behavior and survival. This article suggests that traditional models of stimulus-(neural) response mapping in olfaction may need to be reevaluated and instead motivates the use of dynamical systems theory as a methodology and conceptual framework.
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Affiliation(s)
- Ann-Sophie Barwich
- Cognitive Science Program, Indiana University
- Department of History and Philosophy of Science and Medicine, Indiana University
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32
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Pinotsis DA, Miller EK. In vivo ephaptic coupling allows memory network formation. Cereb Cortex 2023; 33:9877-9895. [PMID: 37420330 PMCID: PMC10472500 DOI: 10.1093/cercor/bhad251] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/20/2023] [Accepted: 06/23/2023] [Indexed: 07/09/2023] Open
Abstract
It is increasingly clear that memories are distributed across multiple brain areas. Such "engram complexes" are important features of memory formation and consolidation. Here, we test the hypothesis that engram complexes are formed in part by bioelectric fields that sculpt and guide the neural activity and tie together the areas that participate in engram complexes. Like the conductor of an orchestra, the fields influence each musician or neuron and orchestrate the output, the symphony. Our results use the theory of synergetics, machine learning, and data from a spatial delayed saccade task and provide evidence for in vivo ephaptic coupling in memory representations.
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Affiliation(s)
- Dimitris A Pinotsis
- Department of Psychology, Centre for Mathematical Neuroscience and Psychology, University of London, London EC1V 0HB, United Kingdom
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
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33
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Natraj N, Seko S, Abiri R, Yan H, Graham Y, Tu-Chan A, Chang EF, Ganguly K. Flexible regulation of representations on a drifting manifold enables long-term stable complex neuroprosthetic control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.551770. [PMID: 37645922 PMCID: PMC10462094 DOI: 10.1101/2023.08.11.551770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what is the representational stability of simple actions, particularly those that are well-rehearsed in humans, and how it changes in new contexts. Using an electrocorticography brain-computer interface (BCI), we found that the mesoscale manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. Interestingly, however, the manifold's absolute location demonstrated day-to-day drift. Strikingly, representational statistics, especially variance, could be flexibly regulated to increase discernability during BCI control without somatotopic changes. Discernability strengthened with practice and was specific to the BCI, demonstrating remarkable contextual specificity. Accounting for drift, and leveraging the flexibility of representations, allowed neuroprosthetic control of a robotic arm and hand for over 7 months without recalibration. Our study offers insight into how electrocorticography can both track representational statistics across long periods and allow long-term complex neuroprosthetic control.
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Affiliation(s)
- Nikhilesh Natraj
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Sarah Seko
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Reza Abiri
- Electrical, Computer and Biomedical Engineering, University of Rhode Island, Rhode Island, USA
| | - Hongyi Yan
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Yasmin Graham
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Adelyn Tu-Chan
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Edward F Chang
- Department of Neurological Surgery, Weill Institute for Neuroscience, University of California-San Francisco, San Francisco, California, USA
| | - Karunesh Ganguly
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
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34
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Vishne G, Gerber EM, Knight RT, Deouell LY. Distinct ventral stream and prefrontal cortex representational dynamics during sustained conscious visual perception. Cell Rep 2023; 42:112752. [PMID: 37422763 PMCID: PMC10530642 DOI: 10.1016/j.celrep.2023.112752] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/12/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
Instances of sustained stationary sensory input are ubiquitous. However, previous work focused almost exclusively on transient onset responses. This presents a critical challenge for neural theories of consciousness, which should account for the full temporal extent of experience. To address this question, we use intracranial recordings from ten human patients with epilepsy to view diverse images of multiple durations. We reveal that, in sensory regions, despite dramatic changes in activation magnitude, the distributed representation of categories and exemplars remains sustained and stable. In contrast, in frontoparietal regions, we find transient content representation at stimulus onset. Our results highlight the connection between the anatomical and temporal correlates of experience. To the extent perception is sustained, it may rely on sensory representations and to the extent perception is discrete, centered on perceptual updating, it may rely on frontoparietal representations.
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Affiliation(s)
- Gal Vishne
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
| | - Edden M Gerber
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Leon Y Deouell
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Department of Psychology, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel.
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35
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Roth ZN, Merriam EP. Representations in human primary visual cortex drift over time. Nat Commun 2023; 14:4422. [PMID: 37479723 PMCID: PMC10361968 DOI: 10.1038/s41467-023-40144-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Primary sensory regions are believed to instantiate stable neural representations, yet a number of recent rodent studies suggest instead that representations drift over time. To test whether sensory representations are stable in human visual cortex, we analyzed a large longitudinal dataset of fMRI responses to images of natural scenes. We fit the fMRI responses using an image-computable encoding model and tested how well the model generalized across sessions. We found systematic changes in model fits that exhibited cumulative drift over many months. Convergent analyses pinpoint changes in neural responsivity as the source of the drift, while population-level representational dissimilarities between visual stimuli were unchanged. These observations suggest that downstream cortical areas may read-out a stable representation, even as representations within V1 exhibit drift.
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Affiliation(s)
- Zvi N Roth
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD, USA.
| | - Elisha P Merriam
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD, USA
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36
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Hassan SI, Bigler S, Siegelbaum SA. Social odor discrimination and its enhancement by associative learning in the hippocampal CA2 region. Neuron 2023; 111:2232-2246.e5. [PMID: 37192623 PMCID: PMC10524117 DOI: 10.1016/j.neuron.2023.04.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/25/2022] [Accepted: 04/21/2023] [Indexed: 05/18/2023]
Abstract
Although the hippocampus is crucial for social memory, how social sensory information is combined with contextual information to form episodic social memories remains unknown. Here, we investigated the mechanisms for social sensory information processing using two-photon calcium imaging from hippocampal CA2 pyramidal neurons (PNs)-which are crucial for social memory-in awake head-fixed mice exposed to social and non-social odors. We found that CA2 PNs represent social odors of individual conspecifics and that these representations are refined during associative social odor-reward learning to enhance the discrimination of rewarded compared with unrewarded odors. Moreover, the structure of the CA2 PN population activity enables CA2 to generalize along categories of rewarded versus unrewarded and social versus non-social odor stimuli. Finally, we found that CA2 is important for learning social but not non-social odor-reward associations. These properties of CA2 odor representations provide a likely substrate for the encoding of episodic social memory.
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Affiliation(s)
- Sami I Hassan
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
| | - Shivani Bigler
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA
| | - Steven A Siegelbaum
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
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37
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Mistry PK, Strock A, Liu R, Young G, Menon V. Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network. Nat Commun 2023; 14:3843. [PMID: 37386013 PMCID: PMC10310708 DOI: 10.1038/s41467-023-39548-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
Number sense, the ability to decipher quantity, forms the foundation for mathematical cognition. How number sense emerges with learning is, however, not known. Here we use a biologically-inspired neural architecture comprising cortical layers V1, V2, V3, and intraparietal sulcus (IPS) to investigate how neural representations change with numerosity training. Learning dramatically reorganized neuronal tuning properties at both the single unit and population levels, resulting in the emergence of sharply-tuned representations of numerosity in the IPS layer. Ablation analysis revealed that spontaneous number neurons observed prior to learning were not critical to formation of number representations post-learning. Crucially, multidimensional scaling of population responses revealed the emergence of absolute and relative magnitude representations of quantity, including mid-point anchoring. These learnt representations may underlie changes from logarithmic to cyclic and linear mental number lines that are characteristic of number sense development in humans. Our findings elucidate mechanisms by which learning builds novel representations supporting number sense.
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Affiliation(s)
- Percy K Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA.
| | - Anthony Strock
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Ruizhe Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Griffin Young
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA.
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94304, USA.
- Wu Tsai Stanford Neuroscience Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA.
- Graduate School of Education, Stanford University, Stanford, CA, 94304, USA.
- Stanford Institute for Human-Centered AI, Stanford University, Stanford, CA, 94304, USA.
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38
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Pancholi R, Ryan L, Peron S. Learning in a sensory cortical microstimulation task is associated with elevated representational stability. Nat Commun 2023; 14:3860. [PMID: 37385989 PMCID: PMC10310840 DOI: 10.1038/s41467-023-39542-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
Sensory cortical representations can be highly dynamic, raising the question of how representational stability impacts learning. We train mice to discriminate the number of photostimulation pulses delivered to opsin-expressing pyramidal neurons in layer 2/3 of primary vibrissal somatosensory cortex. We simultaneously track evoked neural activity across learning using volumetric two-photon calcium imaging. In well-trained animals, trial-to-trial fluctuations in the amount of photostimulus-evoked activity predicted animal choice. Population activity levels declined rapidly across training, with the most active neurons showing the largest declines in responsiveness. Mice learned at varied rates, with some failing to learn the task in the time provided. The photoresponsive population showed greater instability both within and across behavioral sessions among animals that failed to learn. Animals that failed to learn also exhibited a faster deterioration in stimulus decoding. Thus, greater stability in the stimulus response is associated with learning in a sensory cortical microstimulation task.
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Affiliation(s)
- Ravi Pancholi
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA
| | - Lauren Ryan
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA
| | - Simon Peron
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA.
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39
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Koyama Y, Yamamoto T, Hirayama JI, Jimura K, Sadato N, Chikazoe J. Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.12.543130. [PMID: 37425727 PMCID: PMC10326986 DOI: 10.1101/2023.06.12.543130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Functional MRI (fMRI) has been instrumental in understanding how cognitive processes are spatially mapped in the brain, yielding insights about brain regions and functions. However, in case the orthogonality of behavioral or stimulus timing is not guaranteed, the estimated brain maps fail to dissociate each cognitive process, and the resultant maps become unstable. Also, the brain mapping exercise can not provide temporal information on the cognitive process. Here we propose a qualitatively different approach to fMRI analysis, named Cognitive Dynamics Estimation (CDE), that estimates how multiple cognitive processes change over time even when behavior or stimulus logs are unavailable. This method transposes the conventional brain mapping; the brain activity pattern at each time point is subject to regression analysis with data-driven maps of cognitive processes as regressors, resulting in the time series of cognitive processes. The estimated time series captured the fluctuation of intensity and timing of cognitive processes on a trial-by-trial basis, which conventional analysis could not capture. Notably, the estimated time series predicted participants' cognitive ability to perform each psychological task. As an addition to our fMRI analytic toolkit, these results suggest the potential for CDE to elucidate underexplored cognitive phenomena, especially in the temporal domain.
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Affiliation(s)
- Yutaro Koyama
- Department of Psychiatry, University of Wisconsin-Madison, WI 53719, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Tetsuya Yamamoto
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Jun-Ichiro Hirayama
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Ibaraki, 305-8568, Japan
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi 371-8510
| | - Norihiro Sadato
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan
| | - Junichi Chikazoe
- Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
- Araya, Inc., Tokyo, 107-6024, Japan
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40
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Geva N, Deitch D, Rubin A, Ziv Y. Time and experience differentially affect distinct aspects of hippocampal representational drift. Neuron 2023:S0896-6273(23)00378-1. [PMID: 37315556 DOI: 10.1016/j.neuron.2023.05.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/22/2023] [Accepted: 05/08/2023] [Indexed: 06/16/2023]
Abstract
Hippocampal activity is critical for spatial memory. Within a fixed, familiar environment, hippocampal codes gradually change over timescales of days to weeks-a phenomenon known as representational drift. The passage of time and the amount of experience are two factors that profoundly affect memory. However, thus far, it has remained unclear to what extent these factors drive hippocampal representational drift. Here, we longitudinally recorded large populations of hippocampal neurons in mice while they repeatedly explored two different familiar environments that they visited at different time intervals over weeks. We found that time and experience differentially affected distinct aspects of representational drift: the passage of time drove changes in neuronal activity rates, whereas experience drove changes in the cells' spatial tuning. Changes in spatial tuning were context specific and largely independent of changes in activity rates. Thus, our results suggest that representational drift is a multi-faceted process governed by distinct neuronal mechanisms.
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Affiliation(s)
- Nitzan Geva
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Daniel Deitch
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Alon Rubin
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.
| | - Yaniv Ziv
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.
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41
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Khatib D, Ratzon A, Sellevoll M, Barak O, Morris G, Derdikman D. Active experience, not time, determines within-day representational drift in dorsal CA1. Neuron 2023:S0896-6273(23)00387-2. [PMID: 37315557 DOI: 10.1016/j.neuron.2023.05.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 02/22/2023] [Accepted: 05/09/2023] [Indexed: 06/16/2023]
Abstract
Memories of past events can be recalled long after the event, indicating stability. But new experiences are also integrated into existing memories, indicating plasticity. In the hippocampus, spatial representations are known to remain stable but have also been shown to drift over long periods of time. We hypothesized that experience, more than the passage of time, is the driving force behind representational drift. We compared the within-day stability of place cells' representations in dorsal CA1 of the hippocampus of mice traversing two similar, familiar tracks for different durations. We found that the more time the animals spent actively traversing the environment, the greater the representational drift, regardless of the total elapsed time between visits. Our results suggest that spatial representation is a dynamic process, related to the ongoing experiences within a specific context, and is related to memory update rather than to passive forgetting.
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Affiliation(s)
- Dorgham Khatib
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Aviv Ratzon
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Mariell Sellevoll
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Omri Barak
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel; Network Biology Research Laboratories, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Genela Morris
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | - Dori Derdikman
- Department of Neuroscience, The Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa 32000, Israel.
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42
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Wyrick DG, Cain N, Larsen RS, Lecoq J, Valley M, Ahmed R, Bowlus J, Boyer G, Caldejon S, Casal L, Chvilicek M, DePartee M, Groblewski PA, Huang C, Johnson K, Kato I, Larkin J, Lee E, Liang E, Luviano J, Mace K, Nayan C, Nguyen T, Reding M, Seid S, Sevigny J, Stoecklin M, Williford A, Choi H, Garrett M, Mazzucato L. Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.543483. [PMID: 37333203 PMCID: PMC10274646 DOI: 10.1101/2023.06.02.543483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscope's OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.
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Affiliation(s)
- David G Wyrick
- Department of Biology and Institute of Neuroscience, University of Oregon
- Allen Institute, Mindscope program, University of Oregon
| | - Nicholas Cain
- Allen Institute, Mindscope program, University of Oregon
| | | | - Jérôme Lecoq
- Allen Institute, Mindscope program, University of Oregon
| | - Matthew Valley
- Allen Institute, Mindscope program, University of Oregon
| | - Ruweida Ahmed
- Allen Institute, Mindscope program, University of Oregon
| | - Jessica Bowlus
- Allen Institute, Mindscope program, University of Oregon
| | | | | | - Linzy Casal
- Allen Institute, Mindscope program, University of Oregon
| | | | | | | | - Cindy Huang
- Allen Institute, Mindscope program, University of Oregon
| | | | - India Kato
- Allen Institute, Mindscope program, University of Oregon
| | - Josh Larkin
- Allen Institute, Mindscope program, University of Oregon
| | - Eric Lee
- Allen Institute, Mindscope program, University of Oregon
| | | | | | - Kyla Mace
- Allen Institute, Mindscope program, University of Oregon
| | - Chelsea Nayan
- Allen Institute, Mindscope program, University of Oregon
| | | | - Melissa Reding
- Allen Institute, Mindscope program, University of Oregon
| | - Sam Seid
- Allen Institute, Mindscope program, University of Oregon
| | - Joshua Sevigny
- Allen Institute, Mindscope program, University of Oregon
| | | | - Ali Williford
- Allen Institute, Mindscope program, University of Oregon
| | - Hannah Choi
- Allen Institute, Mindscope program, University of Oregon
- School of Mathematics, Georgia Institute of Technology, University of Oregon
| | - Marina Garrett
- Allen Institute, Mindscope program, University of Oregon
| | - Luca Mazzucato
- Department of Biology and Institute of Neuroscience, University of Oregon
- Department of Mathematics and Physics, University of Oregon
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43
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Zhu RJB, Wei XX. Unsupervised approach to decomposing neural tuning variability. Nat Commun 2023; 14:2298. [PMID: 37085524 PMCID: PMC10121715 DOI: 10.1038/s41467-023-37982-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.
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Affiliation(s)
- Rong J B Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China.
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin, Austin, USA.
- Department of Psychology, The University of Texas at Austin, Austin, USA.
- Center for Perceptual Systems, The University of Texas at Austin, Austin, USA.
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, USA.
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44
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Morales GB, di Santo S, Muñoz MA. Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics. Proc Natl Acad Sci U S A 2023; 120:e2208998120. [PMID: 36827262 PMCID: PMC9992863 DOI: 10.1073/pnas.2208998120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 12/31/2022] [Indexed: 02/25/2023] Open
Abstract
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are "quasiuniversal" across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability.
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Affiliation(s)
- Guillermo B. Morales
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
| | - Serena di Santo
- Morton B. Zuckerman Mind Brain Behavior Institute Columbia University, New York, NY10027
| | - Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
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45
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Gilday OD, Mizrahi A. Learning-Induced Odor Modulation of Neuronal Activity in Auditory Cortex. J Neurosci 2023; 43:1375-1386. [PMID: 36650061 PMCID: PMC9987573 DOI: 10.1523/jneurosci.1398-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 01/19/2023] Open
Abstract
Sensory cortices, even of primary regions, are not purely unisensory. Rather, cortical neurons in sensory cortex show various forms of multisensory interactions. While some multisensory interactions naturally co-occur, the combination of others will co-occur through experience. In real life, learning and experience will result in conjunction with seemingly disparate sensory information that ultimately becomes behaviorally relevant, impacting perception, cognition, and action. Here we describe a novel auditory discrimination task in mice, designed to manipulate the expectation of upcoming trials using olfactory cues. We show that, after learning, female mice display a transient period of several days during which they exploit odor-mediated expectations for making correct decisions. Using two-photon calcium imaging of single neurons in auditory cortex (ACx) during behavior, we found that the behavioral effects of odor-mediated expectations are accompanied by an odor-induced modulation of neuronal activity. Further, we find that these effects are manifested differentially, based on the response preference of individual cells. A significant portion of effects, but not all, are consistent with a predictive coding framework. Our data show that learning novel odor-sound associations evoke changes in ACx. We suggest that behaviorally relevant multisensory environments mediate contextual effects as early as ACx.SIGNIFICANCE STATEMENT Natural environments are composed of multisensory objects. It remains unclear whether and how animals learn the regularities of congruent multisensory associations and how these may impact behavior and neural activity. We tested how learned odor-sound associations affected single-neuron responses in auditory cortex. We introduce a novel auditory discrimination task for mice in which odors set different contexts of expectation to upcoming trials. We show that, although the task can be solved purely by sounds, odor-mediated expectation impacts performance. We further show that odors cause a modulation of neuronal activity in auditory cortex, which is correlated with behavior. These results suggest that learning prompts an interaction of odor and sound information as early as sensory cortex.
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Affiliation(s)
- Omri David Gilday
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Adi Mizrahi
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel,
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
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46
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Qin S, Farashahi S, Lipshutz D, Sengupta AM, Chklovskii DB, Pehlevan C. Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning. Nat Neurosci 2023; 26:339-349. [PMID: 36635497 DOI: 10.1038/s41593-022-01225-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 10/28/2022] [Indexed: 01/13/2023]
Abstract
Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform their tasks. This representational 'drift' naturally leads to questions about its causes, dynamics and functions. Here we explore the hypothesis that neural representations optimize a representational objective with a degenerate solution space, and noisy synaptic updates drive the network to explore this (near-)optimal space causing representational drift. We illustrate this idea and explore its consequences in simple, biologically plausible Hebbian/anti-Hebbian network models of representation learning. We find that the drifting receptive fields of individual neurons can be characterized by a coordinated random walk, with effective diffusion constants depending on various parameters such as learning rate, noise amplitude and input statistics. Despite such drift, the representational similarity of population codes is stable over time. Our model recapitulates experimental observations in the hippocampus and posterior parietal cortex and makes testable predictions that can be probed in future experiments.
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Affiliation(s)
- Shanshan Qin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Shiva Farashahi
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - David Lipshutz
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - Anirvan M Sengupta
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
- Department of Physics and Astronomy, Rutgers University, New Brunswick, NJ, USA
| | - Dmitri B Chklovskii
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
- NYU Langone Medical Center, New York, NY, USA
| | - Cengiz Pehlevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
- Center for Brain Science, Harvard University, Cambridge, MA, USA.
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47
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Roüast NM, Schönauer M. Continuously changing memories: a framework for proactive and non-linear consolidation. Trends Neurosci 2023; 46:8-19. [PMID: 36428193 DOI: 10.1016/j.tins.2022.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/10/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
The traditional view of long-term memory is that memory traces mature in a predetermined 'linear' process: their neural substrate shifts from rapidly plastic medial temporal regions towards stable neocortical networks. We propose that memories remain malleable, not by repeated reinstantiations of this linear process but instead via dynamic routes of proactive and non-linear consolidation: memories change, their trajectory is flexible and reversible, and their physical basis develops continuously according to anticipated demands. Studies demonstrating memory updating, increasing hippocampal dependence to support adaptive use, and rapid neocortical plasticity provide evidence for continued non-linear consolidation. Although anticipated demand can affect all stages of memory formation, the extent to which it shapes the physical memory trace repeatedly and proactively will require further dedicated research.
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Affiliation(s)
- Nora Malika Roüast
- Institute for Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany.
| | - Monika Schönauer
- Institute for Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany.
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48
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Shan H, Sompolinsky H. Minimum perturbation theory of deep perceptual learning. Phys Rev E 2022; 106:064406. [PMID: 36671118 DOI: 10.1103/physreve.106.064406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Perceptual learning (PL) involves long-lasting improvement in perceptual tasks following extensive training and is accompanied by modified neuronal responses in sensory cortical areas in the brain. Understanding the dynamics of PL and the resultant synaptic changes is important for causally connecting PL to the observed neural plasticity. This is theoretically challenging because learning-related changes are distributed across many stages of the sensory hierarchy. In this paper, we modeled the sensory hierarchy as a deep nonlinear neural network and studied PL of fine discrimination, a common and well-studied paradigm of PL. Using tools from statistical physics, we developed a mean-field theory of the network in the limit of a large number of neurons and large number of examples. Our theory suggests that, in this thermodynamic limit, the input-output function of the network can be exactly mapped to that of a deep linear network, allowing us to characterize the space of solutions for the task. Surprisingly, we found that modifying synaptic weights in the first layer of the hierarchy is both sufficient and necessary for PL. To address the degeneracy of the space of solutions, we postulate that PL dynamics are constrained by a normative minimum perturbation (MP) principle, which favors weight matrices with minimal changes relative to their prelearning values. Interestingly, MP plasticity induces changes to weights and neural representations in all layers of the network, except for the readout weight vector. While weight changes in higher layers are not necessary for learning, they help reduce overall perturbation to the network. In addition, such plasticity can be learned simply through slow learning. We further elucidate the properties of MP changes and compare them against experimental findings. Overall, our statistical mechanics theory of PL provides mechanistic and normative understanding of several important empirical findings of PL.
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Affiliation(s)
- Haozhe Shan
- Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA and Program in Neuroscience, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Haim Sompolinsky
- Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA and Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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49
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Jensen KT, Kadmon Harpaz N, Dhawale AK, Wolff SBE, Ölveczky BP. Long-term stability of single neuron activity in the motor system. Nat Neurosci 2022; 25:1664-1674. [PMID: 36357811 PMCID: PMC11152193 DOI: 10.1038/s41593-022-01194-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 10/03/2022] [Indexed: 11/12/2022]
Abstract
How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors-both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are generated by single neuron activity patterns that are themselves highly stable.
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Affiliation(s)
- Kristopher T Jensen
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Naama Kadmon Harpaz
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Ashesh K Dhawale
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Steffen B E Wolff
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
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50
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Ordering in heterogeneous connectome weights for visual information processing. Proc Natl Acad Sci U S A 2022; 119:e2216092119. [PMID: 36409900 PMCID: PMC9860139 DOI: 10.1073/pnas.2216092119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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