1
|
Wang B, Torok Z, Duffy A, Bell DG, Wongso S, Velho TAF, Fairhall AL, Lois C. Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation. Nat Neurosci 2024; 27:1176-1186. [PMID: 38684893 DOI: 10.1038/s41593-024-01630-6] [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: 09/16/2022] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic input to neighboring, unmanipulated neurons in the same brain region. A model inspired by the behavioral and electrophysiological findings suggests that unsupervised single-cell and population-level homeostatic plasticity rules can support the functional restoration after large-scale disruption of networks that implement sequential dynamics. These observations suggest the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.
Collapse
Affiliation(s)
- Bo Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Zsofia Torok
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alison Duffy
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - David G Bell
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
- Department of Physics, University of Washington, Seattle, WA, USA
| | - Shelyn Wongso
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Tarciso A F Velho
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
- Department of Physics, University of Washington, Seattle, WA, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| |
Collapse
|
2
|
Delamare G, Zaki Y, Cai DJ, Clopath C. Drift of neural ensembles driven by slow fluctuations of intrinsic excitability. eLife 2024; 12:RP88053. [PMID: 38712831 DOI: 10.7554/elife.88053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024] Open
Abstract
Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.
Collapse
Affiliation(s)
- Geoffroy Delamare
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Yosif Zaki
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Denise J Cai
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, United Kingdom
| |
Collapse
|
3
|
Muysers H, Chen HL, Hahn J, Folschweiller S, Sigurdsson T, Sauer JF, Bartos M. A persistent prefrontal reference frame across time and task rules. Nat Commun 2024; 15:2115. [PMID: 38459033 PMCID: PMC10923947 DOI: 10.1038/s41467-024-46350-4] [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: 10/11/2023] [Accepted: 02/22/2024] [Indexed: 03/10/2024] Open
Abstract
Behavior can be remarkably consistent, even over extended time periods, yet whether this is reflected in stable or 'drifting' neuronal responses to task features remains controversial. Here, we find a persistently active ensemble of neurons in the medial prefrontal cortex (mPFC) of mice that reliably maintains trajectory-specific tuning over several weeks while performing an olfaction-guided spatial memory task. This task-specific reference frame is stabilized during learning, upon which repeatedly active neurons show little representational drift and maintain their trajectory-specific tuning across long pauses in task exposure and across repeated changes in cue-target location pairings. These data thus suggest a 'core ensemble' of prefrontal neurons forming a reference frame of task-relevant space for the performance of consistent behavior over extended periods of time.
Collapse
Affiliation(s)
- Hannah Muysers
- Institute for Physiology I, Medical Faculty, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
| | - Hung-Ling Chen
- Institute for Physiology I, Medical Faculty, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
| | - Johannes Hahn
- Institute of Neurophysiology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Shani Folschweiller
- Institute for Physiology I, Medical Faculty, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
- Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Torfi Sigurdsson
- Institute of Neurophysiology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Jonas-Frederic Sauer
- Institute for Physiology I, Medical Faculty, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany.
| | - Marlene Bartos
- Institute for Physiology I, Medical Faculty, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany.
| |
Collapse
|
4
|
Dyakonova VE. DNA Instability in Neurons: Lifespan Clock and Driver of Evolution. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:1719-1731. [PMID: 38105193 DOI: 10.1134/s0006297923110044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/19/2023] [Accepted: 09/20/2023] [Indexed: 12/19/2023]
Abstract
In the last ten years, the discovery of neuronal DNA postmitotic instability has changed the theoretical landscape in neuroscience and, more broadly, biology. In 2003, A. M. Olovnikov suggested that neuronal DNA is the "initial substrate of aging". Recent experimental data have significantly increased the likelihood of this hypothesis. How does neuronal DNA accumulate damage and in what genome regions? What factors contribute to this process and how are they associated with aging and lifespan? These questions will be discussed in the review. In the course of Metazoan evolution, the instability of neuronal DNA has been accompanied by searching for the pathways to reduce the biological cost of brain activity. Various processes and activities, such as sleep, evolutionary increase in the number of neurons in the vertebrate brain, adult neurogenesis, distribution of neuronal activity, somatic polyploidy, and RNA editing in cephalopods, can be reconsidered in the light of the trade-off between neuronal plasticity and DNA instability in neurons. This topic is of considerable importance for both fundamental neuroscience and translational medicine.
Collapse
Affiliation(s)
- Varvara E Dyakonova
- Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, 119334, Russia.
| |
Collapse
|
5
|
Wybo WAM, Tsai MC, Tran VAK, Illing B, Jordan J, Morrison A, Senn W. NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways. Proc Natl Acad Sci U S A 2023; 120:e2300558120. [PMID: 37523562 PMCID: PMC10410730 DOI: 10.1073/pnas.2300558120] [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: 01/13/2023] [Accepted: 06/14/2023] [Indexed: 08/02/2023] Open
Abstract
While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.
Collapse
Affiliation(s)
- Willem A. M. Wybo
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428Jülich, Germany
| | - Matthias C. Tsai
- Department of Physiology, University of Bern, CH-3012Bern, Switzerland
| | - Viet Anh Khoa Tran
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428Jülich, Germany
- Department of Computer Science - 3, Faculty 1, RWTH Aachen University, DE-52074Aachen, Germany
| | - Bernd Illing
- Laboratory of Computational Neuroscience, École Polytechnique Fédérale de Lausanne, CH-1015Lausanne, Switzerland
| | - Jakob Jordan
- Department of Physiology, University of Bern, CH-3012Bern, Switzerland
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428Jülich, Germany
- Department of Computer Science - 3, Faculty 1, RWTH Aachen University, DE-52074Aachen, Germany
| | - Walter Senn
- Department of Physiology, University of Bern, CH-3012Bern, Switzerland
| |
Collapse
|
6
|
Micou C, O'Leary T. Representational drift as a window into neural and behavioural plasticity. Curr Opin Neurobiol 2023; 81:102746. [PMID: 37392671 DOI: 10.1016/j.conb.2023.102746] [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: 03/11/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 07/03/2023]
Abstract
Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural activity and accompanying physiological changes is due in part to the continuous application of a learning rule at the cellular and population level. Explicit predictions of this drift can be found in neural network models that use iterative learning to optimise weights. Drift therefore provides a measurable signal that can reveal systems-level properties of biological plasticity mechanisms, such as their precision and effective learning rates.
Collapse
Affiliation(s)
- Charles Micou
- Department of Engineering, University of Cambridge, United Kingdom
| | - Timothy O'Leary
- Department of Engineering, University of Cambridge, United Kingdom; Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology Graduate University, Onna, 904-0495, Japan.
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
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: 11] [Impact Index Per Article: 5.5] [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.
Collapse
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.
| |
Collapse
|
11
|
Tuning instability of non-columnar neurons in the salt-and-pepper whisker map in somatosensory cortex. Nat Commun 2022; 13:6611. [PMID: 36329010 PMCID: PMC9633707 DOI: 10.1038/s41467-022-34261-1] [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: 12/02/2021] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
Rodent sensory cortex contains salt-and-pepper maps of sensory features, whose structure is not fully known. Here we investigated the structure of the salt-and-pepper whisker somatotopic map among L2/3 pyramidal neurons in somatosensory cortex, in awake mice performing one-vs-all whisker discrimination. Neurons tuned for columnar (CW) and non-columnar (non-CW) whiskers were spatially intermixed, with co-tuned neurons forming local (20 µm) clusters. Whisker tuning was markedly unstable in expert mice, with 35-46% of pyramidal cells significantly shifting tuning over 5-18 days. Tuning instability was highly concentrated in non-CW tuned neurons, and thus was structured in the map. Instability of non-CW neurons was unchanged during chronic whisker paralysis and when mice discriminated individual whiskers, suggesting it is an inherent feature. Thus, L2/3 combines two distinct components: a stable columnar framework of CW-tuned cells that may promote spatial perceptual stability, plus an intermixed, non-columnar surround with highly unstable tuning.
Collapse
|
12
|
Miehl C, Onasch S, Festa D, Gjorgjieva J. Formation and computational implications of assemblies in neural circuits. J Physiol 2022. [PMID: 36068723 DOI: 10.1113/jp282750] [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: 05/20/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022] Open
Abstract
In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Christoph Miehl
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Sebastian Onasch
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Dylan Festa
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Julijana Gjorgjieva
- Computation in Neural Circuits, Max Planck Institute for Brain Research, 60438, Frankfurt, Germany.,School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| |
Collapse
|
13
|
Driscoll LN, Duncker L, Harvey CD. Representational drift: Emerging theories for continual learning and experimental future directions. Curr Opin Neurobiol 2022; 76:102609. [PMID: 35939861 DOI: 10.1016/j.conb.2022.102609] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/08/2022] [Accepted: 06/23/2022] [Indexed: 11/03/2022]
Abstract
Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks-a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.
Collapse
Affiliation(s)
- Laura N Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Lea Duncker
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
| | | |
Collapse
|
14
|
Hennig MH. The sloppy relationship between neural circuit structure and function. J Physiol 2022. [PMID: 35876720 DOI: 10.1113/jp282757] [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: 04/14/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022] Open
Abstract
Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: Function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are a closely related to the structure and implementation of functionally relevant neural manifolds. Abstract figure legend What are the relationships between noisy and highly variable microscopic neural circuit variables on the one hand and the generation of behaviour on the other? Here it is proposed that an intermediate level of description exists where this relationship can be understood in terms of low-dimensional dynamics. Recordings of neural activity during unconstrained behaviour and the development of new machine learning methods will help to uncover these links. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Matthias H Hennig
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh
| |
Collapse
|
15
|
McNaughton N, Vann SD. Construction of complex memories via parallel distributed cortical–subcortical iterative integration. Trends Neurosci 2022; 45:550-562. [PMID: 35599065 PMCID: PMC7612902 DOI: 10.1016/j.tins.2022.04.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/01/2022] [Accepted: 04/22/2022] [Indexed: 01/08/2023]
Abstract
The construction of complex engrams requires hippocampal-cortical interactions. These include both direct interactions and ones via often-overlooked subcortical loops. Here, we review the anatomical organization of a hierarchy of parallel ‘Papez’ loops through the hypothalamus that are homologous in mammals from rats to humans. These hypothalamic loops supplement direct hippocampal-cortical connections with iterative re-processing paced by theta rhythmicity. We couple existing anatomy and lesion data with theory to propose that recirculation in these loops progressively enhances desired connections, while reducing interference from competing external goals and internal associations. This increases the signal-to-noise ratio in the distributed engrams (neocortical and cerebellar) necessary for complex learning and memory. The hypothalamic nodes provide key motivational input for engram enhancement during consolidation.
Collapse
Affiliation(s)
- Neil McNaughton
- Department of Psychology and Brain Health Research Centre, University of Otago, POB56, Dunedin, New Zealand.
| | - Seralynne D Vann
- School of Psychology, Cardiff University, Park Place, Cardiff, CF10 3AT, UK.
| |
Collapse
|