1
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Yang C, Mammen L, Kim B, Li M, Robson DN, Li JM. A population code for spatial representation in the zebrafish telencephalon. Nature 2024:10.1038/s41586-024-07867-2. [PMID: 39198641 DOI: 10.1038/s41586-024-07867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 07/23/2024] [Indexed: 09/01/2024]
Abstract
Spatial learning in teleost fish requires an intact telencephalon1, a brain region that contains putative analogues to components of the mammalian limbic system (for example, hippocampus)2-4. However, cells fundamental to spatial cognition in mammals-for example, place cells (PCs)5,6-have yet to be established in any fish species. In this study, using tracking microscopy to record brain-wide calcium activity in freely swimming larval zebrafish7, we compute the spatial information content8 of each neuron across the brain. Strikingly, in every recorded animal, cells with the highest spatial specificity were enriched in the zebrafish telencephalon. These PCs form a population code of space from which we can decode the animal's spatial location across time. By continuous recording of population-level activity, we found that the activity manifold of PCs refines and untangles over time. Through systematic manipulation of allothetic and idiothetic cues, we demonstrate that zebrafish PCs integrate multiple sources of information and can flexibly remap to form distinct spatial maps. Using analysis of neighbourhood distance between PCs across environments, we found evidence for a weakly preconfigured network in the telencephalon. The discovery of zebrafish PCs represents a step forward in our understanding of spatial cognition across species and the functional role of the early vertebrate telencephalon.
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Affiliation(s)
- Chuyu Yang
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- University of Tuebingen, Tuebingen, Germany
| | - Lorenz Mammen
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- University of Tuebingen, Tuebingen, Germany
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA
| | - Byoungsoo Kim
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- University of Tuebingen, Tuebingen, Germany
| | - Meng Li
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- INSIDE Institute for Biological and Artificial Intelligence, Shanghai, China
| | - Drew N Robson
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
| | - Jennifer M Li
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
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2
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Wolcott NS, Redman WT, Karpinska M, Jacobs EG, Goard MJ. The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.02.606418. [PMID: 39131375 PMCID: PMC11312567 DOI: 10.1101/2024.08.02.606418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Histological evidence suggests that the estrous cycle exerts a powerful effect on CA1 neurons in mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice over multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with heightened density during periods of greater estradiol (proestrus). These morphological changes were accompanied by greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed enhanced place field stability during proestrus, evident at the single-cell and population level. These results establish the estrous cycle as a driver of large-scale structural and functional plasticity in hippocampal circuits essential for learning and memory.
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Affiliation(s)
- Nora S Wolcott
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - William T Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Intelligent Systems Center, Johns Hopkins University Applied Physics Lab, Laurel, MD 20723, USA
| | - Marie Karpinska
- Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Emily G Jacobs
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Ann S. Bowers Women's Brain Health Initiative, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Michael J Goard
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
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3
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Raju RV, Guntupalli JS, Zhou G, Wendelken C, Lázaro-Gredilla M, George D. Space is a latent sequence: A theory of the hippocampus. SCIENCE ADVANCES 2024; 10:eadm8470. [PMID: 39083616 PMCID: PMC11290523 DOI: 10.1126/sciadv.adm8470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/26/2024] [Indexed: 08/02/2024]
Abstract
Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view.
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4
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Tsay JS, Kim HE, McDougle SD, Taylor JA, Haith A, Avraham G, Krakauer JW, Collins AGE, Ivry RB. Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval. eLife 2024; 13:e91839. [PMID: 39087986 PMCID: PMC11293869 DOI: 10.7554/elife.91839] [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: 08/14/2023] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.
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Affiliation(s)
- Jonathan S Tsay
- Department of Psychology, Carnegie Mellon UniversityPittsburghUnited States
- Neuroscience Institute, Carnegie Mellon UniversityPittsburgUnited States
| | - Hyosub E Kim
- School of Kinesiology, University of British ColumbiaVancouverCanada
| | | | - Jordan A Taylor
- Department of Psychology, Princeton UniversityPrincetonUnited States
| | - Adrian Haith
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
| | - Guy Avraham
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
| | - John W Krakauer
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
- Santa Fe InstituteSanta FeUnited States
| | - Anne GE Collins
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
| | - Richard B Ivry
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
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5
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Dong LL, Fiete IR. Grid Cells in Cognition: Mechanisms and Function. Annu Rev Neurosci 2024; 47:345-368. [PMID: 38684081 DOI: 10.1146/annurev-neuro-101323-112047] [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] [Indexed: 05/02/2024]
Abstract
The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.
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Affiliation(s)
- Ling L Dong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Ila R Fiete
- McGovern Institute and K. Lisa Yang Integrative Computational Neuroscience Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
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6
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Kennedy NGW, Lee JC, Killcross S, Westbrook RF, Holmes NM. Prediction error determines how memories are organized in the brain. eLife 2024; 13:RP95849. [PMID: 39027985 PMCID: PMC11259430 DOI: 10.7554/elife.95849] [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: 07/20/2024] Open
Abstract
How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs.
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Affiliation(s)
| | - Jessica C Lee
- School of Psychology, University of New South WalesSydneyAustralia
- School of Psychology, University of SydneySydneyAustralia
| | - Simon Killcross
- School of Psychology, University of New South WalesSydneyAustralia
| | - R Fred Westbrook
- School of Psychology, University of New South WalesSydneyAustralia
| | - Nathan M Holmes
- School of Psychology, University of New South WalesSydneyAustralia
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7
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McNaughton N, Bannerman D. The homogenous hippocampus: How hippocampal cells process available and potential goals. Prog Neurobiol 2024; 240:102653. [PMID: 38960002 DOI: 10.1016/j.pneurobio.2024.102653] [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: 01/04/2024] [Revised: 04/25/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
We present here a view of the firing patterns of hippocampal cells that is contrary, both functionally and anatomically, to conventional wisdom. We argue that the hippocampus responds to efference copies of goals encoded elsewhere; and that it uses these to detect and resolve conflict or interference between goals in general. While goals can involve space, hippocampal cells do not encode spatial (or other special types of) memory, as such. We also argue that the transverse circuits of the hippocampus operate in an essentially homogeneous way along its length. The apparently different functions of different parts (e.g. memory retrieval versus anxiety) result from the different (situational/motivational) inputs on which those parts perform the same fundamental computational operations. On this view, the key role of the hippocampus is the iterative adjustment, via Papez-like circuits, of synaptic weights in cell assemblies elsewhere.
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Affiliation(s)
- Neil McNaughton
- Department of Psychology and Brain Health Research Centre, University of Otago, POB56, Dunedin 9054, New Zealand.
| | - David Bannerman
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, England, UK
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8
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Miranda M, Silva A, Morici JF, Coletti MA, Belluscio M, Bekinschtein P. Retrieval of contextual memory can be predicted by CA3 remapping and is differentially influenced by NMDAR activity in rat hippocampus subregions. PLoS Biol 2024; 22:e3002706. [PMID: 38950066 PMCID: PMC11244845 DOI: 10.1371/journal.pbio.3002706] [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: 10/05/2023] [Revised: 07/12/2024] [Accepted: 06/12/2024] [Indexed: 07/03/2024] Open
Abstract
Episodic memory is essential to navigate in a changing environment by recalling past events, creating new memories, and updating stored information from experience. Although the mechanisms for acquisition and consolidation have been profoundly studied, much less is known about memory retrieval. Hippocampal spatial representations are key for retrieval of contextually guided episodic memories. Indeed, hippocampal place cells exhibit stable location-specific activity which is thought to support contextual memory, but can also undergo remapping in response to environmental changes. It is unclear if remapping is directly related to the expression of different episodic memories. Here, using an incidental memory recognition task in rats, we showed that retrieval of a contextually guided memory is reflected by the levels of CA3 remapping, demonstrating a clear link between external cues, hippocampal remapping, and episodic memory retrieval that guides behavior. Furthermore, we describe NMDARs as key players in regulating the balance between retrieval and memory differentiation processes by controlling the reactivation of specific memory traces. While an increase in CA3 NMDAR activity boosts memory retrieval, dentate gyrus NMDAR activity enhances memory differentiation. Our results contribute to understanding how the hippocampal circuit sustains a flexible balance between memory formation and retrieval depending on the environmental cues and the internal representations of the individual. They also provide new insights into the molecular mechanisms underlying the contributions of hippocampal subregions to generate this balance.
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Affiliation(s)
- Magdalena Miranda
- Laboratorio de Memoria y Cognición Molecular, Instituto de Neurociencia Cognitiva y Traslacional, CONICET-Fundación INECO-Universidad Favaloro, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Azul Silva
- Laboratorio Bases neuronales del comportamiento, Departamento de Ciencias Fisiológicas, Facultad de Ciencias Médicas, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
- CONICET—Universidad de Buenos Aires, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO Houssay), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Juan Facundo Morici
- Laboratorio de Memoria y Cognición Molecular, Instituto de Neurociencia Cognitiva y Traslacional, CONICET-Fundación INECO-Universidad Favaloro, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Marcos Antonio Coletti
- Laboratorio Bases neuronales del comportamiento, Departamento de Ciencias Fisiológicas, Facultad de Ciencias Médicas, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
- CONICET—Universidad de Buenos Aires, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO Houssay), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Mariano Belluscio
- Laboratorio Bases neuronales del comportamiento, Departamento de Ciencias Fisiológicas, Facultad de Ciencias Médicas, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
- CONICET—Universidad de Buenos Aires, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO Houssay), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Pedro Bekinschtein
- Laboratorio de Memoria y Cognición Molecular, Instituto de Neurociencia Cognitiva y Traslacional, CONICET-Fundación INECO-Universidad Favaloro, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
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9
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Lin H, Zhou J. Hippocampal and orbitofrontal neurons contribute to complementary aspects of associative structure. Nat Commun 2024; 15:5283. [PMID: 38902232 PMCID: PMC11190210 DOI: 10.1038/s41467-024-49652-9] [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: 08/24/2023] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
The ability to establish associations between environmental stimuli is fundamental for higher-order brain functions like state inference and generalization. Both the hippocampus and orbitofrontal cortex (OFC) play pivotal roles in this, demonstrating complex neural activity changes after associative learning. However, how precisely they contribute to representing learned associations remains unclear. Here, we train head-restrained mice to learn four 'odor-outcome' sequence pairs composed of several task variables-the past and current odor cues, sequence structure of 'cue-outcome' arrangement, and the expected outcome; and perform calcium imaging from these mice throughout learning. Sequence-splitting signals that distinguish between paired sequences are detected in both brain regions, reflecting associative memory formation. Critically, we uncover differential contents in represented associations by examining, in each area, how these task variables affect splitting signal generalization between sequence pairs. Specifically, the hippocampal splitting signals are influenced by the combination of past and current cues that define a particular sensory experience. In contrast, the OFC splitting signals are similar between sequence pairs that share the same sequence structure and expected outcome. These findings suggest that the hippocampus and OFC uniquely and complementarily organize the acquired associative structure.
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Affiliation(s)
- Huixin Lin
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Jingfeng Zhou
- Chinese Institute for Brain Research, Beijing, 102206, China.
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10
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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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11
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Zeng YF, Yang KX, Cui Y, Zhu XN, Li R, Zhang H, Wu DC, Stevens RC, Hu J, Zhou N. Conjunctive encoding of exploratory intentions and spatial information in the hippocampus. Nat Commun 2024; 15:3221. [PMID: 38622129 PMCID: PMC11018604 DOI: 10.1038/s41467-024-47570-4] [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: 05/02/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal's exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals' investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.
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Affiliation(s)
- Yi-Fan Zeng
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ke-Xin Yang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yilong Cui
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Xiao-Na Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Rui Li
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Hanqing Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Dong Chuan Wu
- Neuroscience and Brain Disease Center, Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, 404333, Taiwan
- Translational Medicine Research Center, China Medical University Hospital, Taichung City, 404333, Taiwan
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ji Hu
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ning Zhou
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China.
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12
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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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13
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Sosa M, Plitt MH, Giocomo LM. Hippocampal sequences span experience relative to rewards. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.27.573490. [PMID: 38234842 PMCID: PMC10793396 DOI: 10.1101/2023.12.27.573490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.
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Affiliation(s)
- Marielena Sosa
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
| | - Mark H. Plitt
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
- Present address: Department of Molecular and Cell Biology, University of California Berkeley; Berkeley, CA, USA
| | - Lisa M. Giocomo
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
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14
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Ohki T, Kunii N, Chao ZC. Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0. Rev Neurosci 2023; 34:839-868. [PMID: 36960579 DOI: 10.1515/revneuro-2022-0137] [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: 11/15/2022] [Accepted: 02/26/2023] [Indexed: 03/25/2023]
Abstract
There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Naoto Kunii
- Department of Neurosurgery, The University of Tokyo, Tokyo 113-0033, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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15
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Muhle-Karbe PS, Sheahan H, Pezzulo G, Spiers HJ, Chien S, Schuck NW, Summerfield C. Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex. Neuron 2023; 111:3885-3899.e6. [PMID: 37725981 DOI: 10.1016/j.neuron.2023.08.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/10/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023]
Abstract
Humans can navigate flexibly to meet their goals. Here, we asked how the neural representation of allocentric space is distorted by goal-directed behavior. Participants navigated an agent to two successive goal locations in a grid world environment comprising four interlinked rooms, with a contextual cue indicating the conditional dependence of one goal location on another. Examining the neural geometry by which room and context were encoded in fMRI signals, we found that map-like representations of the environment emerged in both hippocampus and neocortex. Cognitive maps in hippocampus and orbitofrontal cortices were compressed so that locations cued as goals were coded together in neural state space, and these distortions predicted successful learning. This effect was captured by a computational model in which current and prospective locations are jointly encoded in a place code, providing a theory of how goals warp the neural representation of space in macroscopic neural signals.
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Affiliation(s)
- Paul S Muhle-Karbe
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK; School of Psychology, University of Birmingham, Birmingham B15 2SA, UK; Centre for Human Brain Health, University of Birmingham, Birmingham B15 2SA, UK.
| | - Hannah Sheahan
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK; Google DeepMind, London EC4A 3TW, UK
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy
| | - Hugo J Spiers
- Department of Experimental Psychology, University College London, London WC1E 6BT, UK
| | - Samson Chien
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Nicolas W Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 14195 Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Aging Research, 14195 Berlin, Germany; Institute of Psychology, Universität Hamburg, 20146 Hamburg, Germany
| | - Christopher Summerfield
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK; Centre for Human Brain Health, University of Birmingham, Birmingham B15 2SA, UK.
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16
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Heald JB, Wolpert DM, Lengyel M. The Computational and Neural Bases of Context-Dependent Learning. Annu Rev Neurosci 2023; 46:233-258. [PMID: 36972611 PMCID: PMC10348919 DOI: 10.1146/annurev-neuro-092322-100402] [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: 03/29/2023]
Abstract
Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
| | - Daniel M Wolpert
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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17
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Low IIC, Giocomo LM, Williams AH. Remapping in a recurrent neural network model of navigation and context inference. eLife 2023; 12:RP86943. [PMID: 37410093 PMCID: PMC10328512 DOI: 10.7554/elife.86943] [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: 07/07/2023] Open
Abstract
Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.
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Affiliation(s)
- Isabel IC Low
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Alex H Williams
- Center for Computational Neuroscience, Flatiron InstituteNew YorkUnited States
- Center for Neural Science, New York UniversityNew YorkUnited States
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18
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Ambrogioni L, Ólafsdóttir HF. Rethinking the hippocampal cognitive map as a meta-learning computational module. Trends Cogn Sci 2023:S1364-6613(23)00128-6. [PMID: 37357064 DOI: 10.1016/j.tics.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/26/2023] [Accepted: 05/24/2023] [Indexed: 06/27/2023]
Abstract
A hallmark of biological intelligence is the ability to adaptively draw on past experience to guide behaviour under novel situations. Yet, the neurobiological principles that underlie this form of meta-learning remain relatively unexplored. In this Opinion, we review the existing literature on hippocampal spatial representations and reinforcement learning theory and describe a novel theoretical framework that aims to account for biological meta-learning. We conjecture that so-called hippocampal cognitive maps of familiar environments are part of a larger meta-representation (meta-map) that encodes information states and sources, which support exploration and provides a foundation for learning. We also introduce concrete hypotheses on how these generic states can be encoded using a principle of superposition.
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Affiliation(s)
- Luca Ambrogioni
- Donders Institute for Brain, Cognition & Behaviour, Radboud Universiteit, Nijmegen, The Netherlands.
| | - H Freyja Ólafsdóttir
- Donders Institute for Brain, Cognition & Behaviour, Radboud Universiteit, Nijmegen, The Netherlands.
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19
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Miller AMP, Jacob AD, Ramsaran AI, De Snoo ML, Josselyn SA, Frankland PW. Emergence of a predictive model in the hippocampus. Neuron 2023; 111:1952-1965.e5. [PMID: 37015224 PMCID: PMC10293047 DOI: 10.1016/j.neuron.2023.03.011] [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: 09/08/2022] [Revised: 01/23/2023] [Accepted: 03/08/2023] [Indexed: 04/05/2023]
Abstract
The brain organizes experiences into memories that guide future behavior. Hippocampal CA1 population activity is hypothesized to reflect predictive models that contain information about future events, but little is known about how they develop. We trained mice on a series of problems with or without a common statistical structure to observe how memories are formed and updated. Mice that learned structured problems integrated their experiences into a predictive model that contained the solutions to upcoming novel problems. Retrieving the model during learning improved discrimination accuracy and facilitated learning. Using calcium imaging to track CA1 activity during learning, we found that hippocampal ensemble activity became more stable as mice formed a predictive model. The hippocampal ensemble was reactivated during training and incorporated new activity patterns from each training problem. These results show how hippocampal activity supports building predictive models by organizing new information with respect to existing memories.
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Affiliation(s)
- Adam M P Miller
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alex D Jacob
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Adam I Ramsaran
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Mitchell L De Snoo
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sheena A Josselyn
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Brain, Mind, & Consciousness Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Paul W Frankland
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Child & Brain Development Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
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20
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Crivelli-Decker J, Clarke A, Park SA, Huffman DJ, Boorman ED, Ranganath C. Goal-oriented representations in the human hippocampus during planning and navigation. Nat Commun 2023; 14:2946. [PMID: 37221176 PMCID: PMC10206082 DOI: 10.1038/s41467-023-35967-6] [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: 09/07/2021] [Accepted: 01/10/2023] [Indexed: 05/25/2023] Open
Abstract
Recent work in cognitive and systems neuroscience has suggested that the hippocampus might support planning, imagination, and navigation by forming cognitive maps that capture the abstract structure of physical spaces, tasks, and situations. Navigation involves disambiguating similar contexts, and the planning and execution of a sequence of decisions to reach a goal. Here, we examine hippocampal activity patterns in humans during a goal-directed navigation task to investigate how contextual and goal information are incorporated in the construction and execution of navigational plans. During planning, hippocampal pattern similarity is enhanced across routes that share a context and a goal. During navigation, we observe prospective activation in the hippocampus that reflects the retrieval of pattern information related to a key-decision point. These results suggest that, rather than simply representing overlapping associations or state transitions, hippocampal activity patterns are shaped by context and goals.
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Affiliation(s)
- Jordan Crivelli-Decker
- Center for Neuroscience, University of California, Davis, CA, USA.
- Department of Psychology, University of California, Davis, CA, USA.
| | - Alex Clarke
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Seongmin A Park
- Center for Neuroscience, University of California, Davis, CA, USA
- Center for Mind and Brain, University of California, Davis, CA, USA
| | - Derek J Huffman
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychology, Colby College, Waterville, ME, USA
| | - Erie D Boorman
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Charan Ranganath
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychology, University of California, Davis, CA, USA
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21
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Low II, Giocomo LM, Williams AH. Remapping in a recurrent neural network model of navigation and context inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525596. [PMID: 36747825 PMCID: PMC9900889 DOI: 10.1101/2023.01.25.525596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.
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Affiliation(s)
- Isabel I.C. Low
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | | | - Alex H. Williams
- Center for Computational Neuroscience, Flatiron Institute
- Center for Neural Science, New York University
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22
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Kang YHR, Wolpert DM, Lengyel M. Spatial uncertainty and environmental geometry in navigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526278. [PMID: 36778354 PMCID: PMC9915518 DOI: 10.1101/2023.01.30.526278] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Variations in the geometry of the environment, such as the shape and size of an enclosure, have profound effects on navigational behavior and its neural underpinning. Here, we show that these effects arise as a consequence of a single, unifying principle: to navigate efficiently, the brain must maintain and update the uncertainty about one's location. We developed an image-computable Bayesian ideal observer model of navigation, continually combining noisy visual and self-motion inputs, and a neural encoding model optimized to represent the location uncertainty computed by the ideal observer. Through mathematical analysis and numerical simulations, we show that the ideal observer accounts for a diverse range of sometimes paradoxical distortions of human homing behavior in anisotropic and deformed environments, including 'boundary tethering', and its neural encoding accounts for distortions of rodent grid cell responses under identical environmental manipulations. Our results demonstrate that spatial uncertainty plays a key role in navigation.
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Affiliation(s)
- Yul HR Kang
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
| | - Daniel M Wolpert
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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23
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Abstract
A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas.
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24
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Duvelle É, Grieves RM, van der Meer MAA. Temporal context and latent state inference in the hippocampal splitter signal. eLife 2023; 12:e82357. [PMID: 36622350 PMCID: PMC9829411 DOI: 10.7554/elife.82357] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/06/2022] [Indexed: 01/10/2023] Open
Abstract
The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central goal is to understand what the core computations supporting these functions are, and how these computations are realized in the collective action of single neurons. A potential access point into this issue is provided by 'splitter cells', hippocampal neurons that fire differentially on the overlapping segment of trajectories that differ in their past and/or future. However, the literature on splitter cells has been fragmented and confusing, owing to differences in terminology, behavioral tasks, and analysis methods across studies. In this review, we synthesize consistent findings from this literature, establish a common set of terms, and translate between single-cell and ensemble perspectives. Most importantly, we examine the combined findings through the lens of two major theoretical ideas about hippocampal function: representation of temporal context and latent state inference. We find that unique signature properties of each of these models are necessary to account for the data, but neither theory, by itself, explains all of its features. Specifically, the temporal gradedness of the splitter signal is strong support for temporal context, but is hard to explain using state models, while its flexibility and task-dependence is naturally accounted for using state inference, but poses a challenge otherwise. These theories suggest a number of avenues for future work, and we believe their application to splitter cells is a timely and informative domain for testing and refining theoretical ideas about hippocampal function.
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Affiliation(s)
- Éléonore Duvelle
- Department of Psychological and Brain Sciences, Dartmouth CollegeHanoverUnited States
| | - Roddy M Grieves
- Department of Psychological and Brain Sciences, Dartmouth CollegeHanoverUnited States
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25
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Heald JB, Lengyel M, Wolpert DM. Contextual inference in learning and memory. Trends Cogn Sci 2023; 27:43-64. [PMID: 36435674 PMCID: PMC9789331 DOI: 10.1016/j.tics.2022.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022]
Abstract
Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary.
| | - Daniel M Wolpert
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
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26
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Sun Y, Giocomo LM. Neural circuit dynamics of drug-context associative learning in the mouse hippocampus. Nat Commun 2022; 13:6721. [PMID: 36344498 PMCID: PMC9640587 DOI: 10.1038/s41467-022-34114-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
The environmental context associated with previous drug consumption is a potent trigger for drug relapse. However, the mechanism by which neural representations of context are modified to incorporate information associated with drugs of abuse remains unknown. Using longitudinal calcium imaging in freely behaving mice, we find that unlike the associative learning of natural reward, drug-context associations for psychostimulants and opioids are encoded in a specific subset of hippocampal neurons. After drug conditioning, these neurons weakened their spatial coding for the non-drug paired context, resulting in an orthogonal representation for the drug versus non-drug context that was predictive of drug-seeking behavior. Furthermore, these neurons were selected based on drug-spatial experience and were exclusively tuned to animals' allocentric position. Together, this work reveals how drugs of abuse alter the hippocampal circuit to encode drug-context associations and points to the possibility of targeting drug-associated memory in the hippocampus.
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Affiliation(s)
- Yanjun Sun
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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27
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Wirtshafter HS, Wilson MA. Artificial intelligence insights into hippocampal processing. Front Comput Neurosci 2022; 16:1044659. [DOI: 10.3389/fncom.2022.1044659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.
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28
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Khona M, Fiete IR. Attractor and integrator networks in the brain. Nat Rev Neurosci 2022; 23:744-766. [DOI: 10.1038/s41583-022-00642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2022] [Indexed: 11/06/2022]
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29
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Ross TW, Easton A. Rats use strategies to make object choices in spontaneous object recognition tasks. Sci Rep 2022; 12:16973. [PMID: 36216920 PMCID: PMC9550825 DOI: 10.1038/s41598-022-21537-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/28/2022] [Indexed: 12/29/2022] Open
Abstract
Rodent spontaneous object recognition (SOR) paradigms are widely used to study the mechanisms of complex memory in many laboratories. Due to the absence of explicit reinforcement in these tasks, there is an underlying assumption that object exploratory behaviour is 'spontaneous'. However, rodents can strategise, readily adapting their behaviour depending on the current information available and prior predications formed from learning and memory. Here, using the object-place-context (episodic-like) recognition task and novel analytic methods relying on multiple trials within a single session, we demonstrate that rats use a context-based or recency-based object recognition strategy for the same types of trials, depending on task conditions. Exposure to occasional ambiguous conditions changed animals' responses towards a recency-based preference. However, more salient and predictable conditions led to animals exploring objects on the basis of episodic novelty reliant on contextual information. The results have important implications for future research using SOR tasks, especially in the way experimenters design, analyse and interpret object recognition experiments in non-human animals.
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Affiliation(s)
- T W Ross
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, UK.
- Centre for Learning and Memory Processes, Durham University, Durham, UK.
| | - A Easton
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, UK
- Centre for Learning and Memory Processes, Durham University, Durham, UK
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30
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Whittington JCR, McCaffary D, Bakermans JJW, Behrens TEJ. How to build a cognitive map. Nat Neurosci 2022; 25:1257-1272. [PMID: 36163284 DOI: 10.1038/s41593-022-01153-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 07/25/2022] [Indexed: 11/08/2022]
Abstract
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal-cortical interactions and beyond.
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Affiliation(s)
- James C R Whittington
- Department of Applied Physics, Stanford University, Stanford, CA, USA.
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - David McCaffary
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jacob J W Bakermans
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Timothy E J Behrens
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
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31
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Stoianov I, Maisto D, Pezzulo G. The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning. Prog Neurobiol 2022; 217:102329. [PMID: 35870678 DOI: 10.1016/j.pneurobio.2022.102329] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
Abstract
We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that the hippocampal generative model is endowed with inductive biases to identify individual items of experience (first hierarchical layer), organize them into sequences (second layer) and cluster them into maps (third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, which supports the continual learning of multiple sequential experiences. We show that the model learns and efficiently retains multiple spatial navigation trajectories, by organizing them into spatial maps. Furthermore, the model reproduces flexible and prospective aspects of hippocampal dynamics that are challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination.
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Affiliation(s)
- Ivilin Stoianov
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Domenico Maisto
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
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32
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Glitz L, Juechems K, Summerfield C, Garrett N. Model Sharing in the Human Medial Temporal Lobe. J Neurosci 2022; 42:5410-5426. [PMID: 35606146 PMCID: PMC7613027 DOI: 10.1523/jneurosci.1978-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/20/2022] [Accepted: 04/23/2022] [Indexed: 11/21/2022] Open
Abstract
Effective planning involves knowing where different actions take us. However, natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16 female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low-dimensional (cue independent) and a high-dimensional (cue specific) representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses were less correlated between trials with differing input features but similar state associations in the high dimensional than in the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared with negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world.SIGNIFICANCE STATEMENT Effective planning involves maintaining an accurate model of which actions take us to which locations. But in a world awash with information, mapping actions to states with the right level of complexity is critical. Using a new decision-making "heist task" in conjunction with computational modeling and fMRI, we show that patterns of BOLD responses in the medial temporal lobe-a brain region key for prospective planning-become less sensitive to the presence of visual features when these are irrelevant to the task at hand. By flexibly adapting the complexity of task-state representations in this way, state-action mappings learned under one set of features can be used to plan in the presence of others.
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Affiliation(s)
- Leonie Glitz
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6HG, United Kingdom
| | - Keno Juechems
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6HG, United Kingdom
| | | | - Neil Garrett
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6HG, United Kingdom
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
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33
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Abstract
People with damage to the orbitofrontal cortex (OFC) have specific problems making decisions, whereas their other cognitive functions are spared. Neurophysiological studies have shown that OFC neurons fire in proportion to the value of anticipated outcomes. Thus, a central role of the OFC is to guide optimal decision-making by signalling values associated with different choices. Until recently, this view of OFC function dominated the field. New data, however, suggest that the OFC may have a much broader role in cognition by representing cognitive maps that can be used to guide behaviour and that value is just one of many variables that are important for behavioural control. In this Review, we critically evaluate these two alternative accounts of OFC function and examine how they might be reconciled.
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Affiliation(s)
- Eric B Knudsen
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA
| | - Joni D Wallis
- Department of Psychology and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA.
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Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments. J Neurosci 2022; 42:5268-5280. [PMID: 35641190 PMCID: PMC9236289 DOI: 10.1523/jneurosci.3221-20.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 04/11/2022] [Accepted: 05/10/2022] [Indexed: 12/31/2022] Open
Abstract
Hippocampal place cells form a map of the environment of an animal. Changes in the hippocampal map can be brought about in a number of ways, including changes to the environment, task, internal state of the subject, and the passage of time. These changes in the hippocampal map have been called remapping. In this study, we examine remapping during repeated exposure to the same environment. Different animals can have different remapping responses to the same changes. This variability across animals in remapping behavior is not well understood. In this work, we analyzed electrophysiological recordings from the CA3 region of the hippocampus performed by Alme et al. (2014), in which five male rats were exposed to 11 different environments, including a variety of repetitions of those environments. To compare the hippocampal maps between two experiences, we computed average rate map correlation coefficients. We found changes in the hippocampal maps between different sessions in the same environment. These changes consisted of partial remapping, a form of remapping in which some place cells maintain their place fields, whereas other place cells remap their place fields. Each animal exhibited partial remapping differently. We discovered that the heterogeneity in hippocampal representational changes across animals is structured; individual animals had consistently different levels of partial remapping across a range of independent comparisons. Our findings highlight that partial hippocampal remapping between repeated environments depends on animal-specific factors.SIGNIFICANCE STATEMENT Context identification is a difficult problem. Animals are not provided with objective context identity labels, so they must infer which experiences come from which contexts. Different animals may have different strategies for performing this inference. We find that different animals have stereotypically different extents of partial hippocampal remapping, a neural correlate of subjective assessment of context identity.
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35
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Taniguchi A, Fukawa A, Yamakawa H. Hippocampal formation-inspired probabilistic generative model. Neural Netw 2022; 151:317-335. [DOI: 10.1016/j.neunet.2022.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 03/09/2022] [Accepted: 04/03/2022] [Indexed: 11/25/2022]
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Nyberg N, Duvelle É, Barry C, Spiers HJ. Spatial goal coding in the hippocampal formation. Neuron 2022; 110:394-422. [PMID: 35032426 DOI: 10.1016/j.neuron.2021.12.012] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/18/2021] [Accepted: 12/08/2021] [Indexed: 12/22/2022]
Abstract
The mammalian hippocampal formation contains several distinct populations of neurons involved in representing self-position and orientation. These neurons, which include place, grid, head direction, and boundary-vector cells, are thought to collectively instantiate cognitive maps supporting flexible navigation. However, to flexibly navigate, it is necessary to also maintain internal representations of goal locations, such that goal-directed routes can be planned and executed. Although it has remained unclear how the mammalian brain represents goal locations, multiple neural candidates have recently been uncovered during different phases of navigation. For example, during planning, sequential activation of spatial cells may enable simulation of future routes toward the goal. During travel, modulation of spatial cells by the prospective route, or by distance and direction to the goal, may allow maintenance of route and goal-location information, supporting navigation on an ongoing basis. As the goal is approached, an increased activation of spatial cells may enable the goal location to become distinctly represented within cognitive maps, aiding goal localization. Lastly, after arrival at the goal, sequential activation of spatial cells may represent the just-taken route, enabling route learning and evaluation. Here, we review and synthesize these and other evidence for goal coding in mammalian brains, relate the experimental findings to predictions from computational models, and discuss outstanding questions and future challenges.
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Affiliation(s)
- Nils Nyberg
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, UK.
| | - Éléonore Duvelle
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Caswell Barry
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Hugo J Spiers
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, UK.
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37
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Collins AGE, Shenhav A. Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology 2022; 47:104-118. [PMID: 34453117 PMCID: PMC8617262 DOI: 10.1038/s41386-021-01126-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 07/14/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023]
Abstract
An organism's survival depends on its ability to learn about its environment and to make adaptive decisions in the service of achieving the best possible outcomes in that environment. To study the neural circuits that support these functions, researchers have increasingly relied on models that formalize the computations required to carry them out. Here, we review the recent history of computational modeling of learning and decision-making, and how these models have been used to advance understanding of prefrontal cortex function. We discuss how such models have advanced from their origins in basic algorithms of updating and action selection to increasingly account for complexities in the cognitive processes required for learning and decision-making, and the representations over which they operate. We further discuss how a deeper understanding of the real-world complexities in these computations has shed light on the fundamental constraints on optimal behavior, and on the complex interactions between corticostriatal pathways to determine such behavior. The continuing and rapid development of these models holds great promise for understanding the mechanisms by which animals adapt to their environments, and what leads to maladaptive forms of learning and decision-making within clinical populations.
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Affiliation(s)
- Anne G E Collins
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
| | - Amitai Shenhav
- Department of Cognitive, Linguistic, & Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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38
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Contextual inference underlies the learning of sensorimotor repertoires. Nature 2021; 600:489-493. [PMID: 34819674 DOI: 10.1038/s41586-021-04129-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 10/13/2021] [Indexed: 11/09/2022]
Abstract
ASBTRACT Humans spend a lifetime learning, storing and refining a repertoire of motor memories. For example, through experience, we become proficient at manipulating a large range of objects with distinct dynamical properties. However, it is unknown what principle underlies how our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a theory of motor learning based on the key principle that memory creation, updating and expression are all controlled by a single computation-contextual inference. Our theory reveals that adaptation can arise both by creating and updating memories (proper learning) and by changing how existing memories are differentially expressed (apparent learning). This insight enables us to account for key features of motor learning that had no unified explanation: spontaneous recovery1, savings2, anterograde interference3, how environmental consistency affects learning rate4,5 and the distinction between explicit and implicit learning6. Critically, our theory also predicts new phenomena-evoked recovery and context-dependent single-trial learning-which we confirm experimentally. These results suggest that contextual inference, rather than classical single-context mechanisms1,4,7-9, is the key principle underlying how a diverse set of experiences is reflected in our motor behaviour.
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39
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Ross TW, Easton A. The Hippocampal Horizon: Constructing and Segmenting Experience for Episodic Memory. Neurosci Biobehav Rev 2021; 132:181-196. [PMID: 34826509 DOI: 10.1016/j.neubiorev.2021.11.038] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/29/2022]
Abstract
How do we recollect specific events that have occurred during continuous ongoing experience? There is converging evidence from non-human animals that spatially modulated cellular activity of the hippocampal formation supports the construction of ongoing events. On the other hand, recent human oriented event cognition models have outlined that our experience is segmented into discrete units, and that such segmentation can operate on shorter or longer timescales. Here, we describe a unification of how these dynamic physiological mechanisms of the hippocampus relate to ongoing externally and internally driven event segmentation, facilitating the demarcation of specific moments during experience. Our cross-species interdisciplinary approach offers a novel perspective in the way we construct and remember specific events, leading to the generation of many new hypotheses for future research.
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Affiliation(s)
- T W Ross
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, United Kingdom; Centre for Learning and Memory Processes, Durham University, United Kingdom.
| | - A Easton
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, United Kingdom; Centre for Learning and Memory Processes, Durham University, United Kingdom
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40
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Wang S, Feng SF, Bornstein AM. Mixing memory and desire: How memory reactivation supports deliberative decision-making. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 13:e1581. [PMID: 34665529 DOI: 10.1002/wcs.1581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/24/2021] [Accepted: 09/16/2021] [Indexed: 11/09/2022]
Abstract
Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affects deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted based on their uncertainty. We close by reviewing related findings and identifying areas for further research. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Neuroscience > Computation.
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Affiliation(s)
- Shaoming Wang
- Department of Psychology, New York University, New York, New York, USA
| | - Samuel F Feng
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, UAE.,Khalifa University Centre for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Aaron M Bornstein
- Department of Cognitive Sciences, University of California-Irvine, Irvine, California, USA.,Center for the Neurobiology of Learning & Memory, University of California-Irvine, Irvine, California, USA.,Institute for Mathematical Behavioral Sciences, University of California-Irvine, Irvine, California, USA
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41
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Chen HT, Manning JR, van der Meer MAA. Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. Curr Biol 2021; 31:4293-4304.e5. [PMID: 34428470 DOI: 10.1016/j.cub.2021.07.061] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/27/2021] [Accepted: 07/26/2021] [Indexed: 11/24/2022]
Abstract
The rodent hippocampus constructs statistically independent representations across environments ("global remapping") and assigns individual neuron firing fields to locations within an environment in an apparently random fashion, processes thought to contribute to the role of the hippocampus in episodic memory. This random mapping implies that it should be challenging to predict hippocampal encoding of a given experience in one subject based on the encoding of that same experience in another subject. Contrary to this prediction, we find that by constructing a common representational space across rats in which neural activity is aligned using geometric operations (rotation, reflection, and translation; "hyperalignment"), we can predict data of "right" trials (R) on a T-maze in a target rat based on (1) the "left" trials (L) of the target rat and (2) the relationship between L and R trials from a different source rat. These cross-subject predictions relied on ensemble activity patterns, including both firing rate and field location, and outperformed a number of control mappings, such as those based on permuted data that broke the relationship between L and R activity for individual neurons and those based solely on within-subject prediction. This work constitutes proof of principle for successful cross-subject prediction of ensemble activity patterns in the hippocampus and provides new insights in understanding how different experiences are structured, enabling further work identifying what aspects of experience encoding are shared versus unique to an individual.
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Affiliation(s)
- Hung-Tu Chen
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Jeremy R Manning
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
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42
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Fetterhoff D, Sobolev A, Leibold C. Graded remapping of hippocampal ensembles under sensory conflicts. Cell Rep 2021; 36:109661. [PMID: 34525357 DOI: 10.1016/j.celrep.2021.109661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/09/2021] [Accepted: 08/13/2021] [Indexed: 11/18/2022] Open
Abstract
Hippocampal place cells are thought to constitute a cognitive map of space derived from multimodal sensory inputs. Alteration of allocentric (visual) cues in a fixed environment is known to induce modulations of place cell activity to varying degrees from rate changes to global remapping. To determine how hippocampal ensembles combine multimodal sensory cues, we examine hippocampal CA1 remapping in Mongolian gerbils in a 1D virtual reality experiment, during which self-motion cues (locomotor, vestibular, and optic flow information) and allocentric visual cues are altered. We observe that self-motion cues are over-represented, but responsiveness to allocentric visual cues, although task-irrelevant, elicits both rate and global remapping in the hippocampal ensemble. We propose that remapping can be reconciled by considering global, partial, and rate remapping on a continuous scale on which the graded change of activity in the entire CA1 population can be interpreted as the expectancy about the animal's spatial environment.
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Affiliation(s)
- Dustin Fetterhoff
- Department Biologie II, Ludwig-Maximilians-Universität München, 82152 Munich, Germany.
| | - Andrey Sobolev
- Department Biologie II, Ludwig-Maximilians-Universität München, 82152 Munich, Germany
| | - Christian Leibold
- Department Biologie II, Ludwig-Maximilians-Universität München, 82152 Munich, Germany; Bernstein Center for Computational Neuroscience Munich, 82152 Munich, Germany
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43
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Yu LQ, Wilson RC, Nassar MR. Adaptive learning is structure learning in time. Neurosci Biobehav Rev 2021; 128:270-281. [PMID: 34144114 PMCID: PMC8422504 DOI: 10.1016/j.neubiorev.2021.06.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/19/2021] [Accepted: 06/11/2021] [Indexed: 10/21/2022]
Abstract
People use information flexibly. They often combine multiple sources of relevant information over time in order to inform decisions with little or no interference from intervening irrelevant sources. They adjust the degree to which they use new information over time rationally in accordance with environmental statistics and their own uncertainty. They can even use information gained in one situation to solve a problem in a very different one. Learning flexibly rests on the ability to infer the context at a given time, and therefore knowing which pieces of information to combine and which to separate. We review the psychological and neural mechanisms behind adaptive learning and structure learning to outline how people pool together relevant information, demarcate contexts, prevent interference between information collected in different contexts, and transfer information from one context to another. By examining all of these processes through the lens of optimal inference we bridge concepts from multiple fields to provide a unified multi-system view of how the brain exploits structure in time to optimize learning.
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Affiliation(s)
- Linda Q Yu
- Carney Institute for Brain Sciences, Brown University, 164 Angell Street, Providence, RI, 02912, USA.
| | - Robert C Wilson
- Department of Psychology, University of Arizona, Tucson, AZ, 85721, USA
| | - Matthew R Nassar
- Carney Institute for Brain Sciences, Brown University, 164 Angell Street, Providence, RI, 02912, USA
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44
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Wikenheiser AM, Gardner MPH, Mueller LE, Schoenbaum G. Spatial Representations in Rat Orbitofrontal Cortex. J Neurosci 2021; 41:6933-6945. [PMID: 34210776 PMCID: PMC8360685 DOI: 10.1523/jneurosci.0830-21.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 06/09/2021] [Accepted: 06/20/2021] [Indexed: 01/03/2023] Open
Abstract
The orbitofrontal cortex (OFC) and hippocampus share striking cognitive and functional similarities. As a result, both structures have been proposed to encode "cognitive maps" that provide useful scaffolds for planning complex behaviors. However, while this function has been exemplified by spatial coding in neurons of hippocampal regions-particularly place and grid cells-spatial representations in the OFC have been investigated far less. Here we sought to address this by recording OFC neurons from male rats engaged in an open-field foraging task like that originally developed to characterize place fields in rodent hippocampal neurons. Single-unit activity was recorded as rats searched for food pellets scattered randomly throughout a large enclosure. In some sessions, particular flavors of food occurred more frequently in particular parts of the enclosure; in others, only a single flavor was used. OFC neurons showed spatially localized firing fields in both conditions, and representations changed between flavored and unflavored foraging periods in a manner reminiscent of remapping in the hippocampus. Compared with hippocampal recordings taken under similar behavioral conditions, OFC spatial representations were less temporally reliable, and there was no significant evidence of grid tuning in OFC neurons. These data confirm that OFC neurons show spatial firing fields in a large, two-dimensional environment in a manner similar to hippocampus. Consistent with the focus of the OFC on biological meaning and goals, spatial coding was weaker than in hippocampus and influenced by outcome identity.SIGNIFICANCE STATEMENT The orbitofrontal cortex (OFC) and hippocampus have both been proposed to encode "cognitive maps" that provide useful scaffolds for planning complex behaviors. This function is exemplified by place and grid cells identified in hippocampus, the activity of which maps spatial environments. The current study directly demonstrates very similar, though not identical, spatial representatives in OFC neurons, confirming that OFC-like hippocampus-can represent a spatial map under the appropriate experimental conditions.
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Affiliation(s)
- Andrew M Wikenheiser
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
- Brain Research Institute, University of California, Los Angeles, Los Angeles, California 90095
| | - Matthew P H Gardner
- Behavioral Neurophysiology Research Section, Cellular Neurobiology Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland 21224
| | - Lauren E Mueller
- Behavioral Neurophysiology Research Section, Cellular Neurobiology Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland 21224
| | - Geoffrey Schoenbaum
- Behavioral Neurophysiology Research Section, Cellular Neurobiology Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland 21224
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45
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Wanjia G, Favila SE, Kim G, Molitor RJ, Kuhl BA. Abrupt hippocampal remapping signals resolution of memory interference. Nat Commun 2021; 12:4816. [PMID: 34376652 PMCID: PMC8355182 DOI: 10.1038/s41467-021-25126-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/19/2021] [Indexed: 11/09/2022] Open
Abstract
Remapping refers to a decorrelation of hippocampal representations of similar spatial environments. While it has been speculated that remapping may contribute to the resolution of episodic memory interference in humans, direct evidence is surprisingly limited. We tested this idea using high-resolution, pattern-based fMRI analyses. Here we show that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning. The magnitude of this learning-related decorrelation was predicted by the amount of pattern overlap during initial stages of learning, with greater initial overlap leading to stronger decorrelation. Finally, we show that remapped activity patterns carry relatively more information about learned episodic associations compared to competing associations, further validating the learning-related significance of remapping. Collectively, these findings establish a critical link between hippocampal remapping and episodic memory interference and provide insight into why remapping occurs.
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Affiliation(s)
- Guo Wanjia
- Department of Psychology, University of Oregon, Eugene, OR, USA.
| | - Serra E Favila
- Department of Psychology, Columbia University, New York, NY, USA
| | - Ghootae Kim
- Korea Brain Research Institute, Dong-gu, Daegu, Republic of Korea
| | | | - Brice A Kuhl
- Department of Psychology, University of Oregon, Eugene, OR, USA.
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46
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Low IIC, Williams AH, Campbell MG, Linderman SW, Giocomo LM. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 2021; 109:2967-2980.e11. [PMID: 34363753 DOI: 10.1016/j.neuron.2021.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/26/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022]
Abstract
Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations.
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Affiliation(s)
- Isabel I C Low
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Alex H Williams
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Malcolm G Campbell
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Scott W Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
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Knudsen EB, Wallis JD. Hippocampal neurons construct a map of an abstract value space. Cell 2021; 184:4640-4650.e10. [PMID: 34348112 DOI: 10.1016/j.cell.2021.07.010] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 05/11/2021] [Accepted: 07/08/2021] [Indexed: 02/06/2023]
Abstract
The hippocampus is thought to encode a "cognitive map," a structural organization of knowledge about relationships in the world. Place cells, spatially selective hippocampal neurons that have been extensively studied in rodents, are one component of this map, describing the relative position of environmental features. However, whether this map extends to abstract, cognitive information remains unknown. Using the relative reward value of cues to define continuous "paths" through an abstract value space, we show that single neurons in primate hippocampus encode this space through value place fields, much like a rodent's place neurons encode paths through physical space. Value place fields remapped when cues changed but also became increasingly correlated across contexts, allowing maps to become generalized. Our findings help explain the critical contribution of the hippocampus to value-based decision-making, providing a mechanism by which knowledge of relationships in the world can be incorporated into reward predictions for guiding decisions.
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Affiliation(s)
- Eric B Knudsen
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA.
| | - Joni D Wallis
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA; Department of Psychology, University of California at Berkeley, Berkeley, CA, USA
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48
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Yim MY, Sadun LA, Fiete IR, Taillefumier T. Place-cell capacity and volatility with grid-like inputs. eLife 2021; 10:e62702. [PMID: 34028354 PMCID: PMC8294848 DOI: 10.7554/elife.62702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/28/2021] [Indexed: 01/07/2023] Open
Abstract
What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell's repertoire - how many field arrangements it can realize without external cues while its grid inputs are unique - and derive its capacity - the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.
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Affiliation(s)
- Man Yi Yim
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Neuroscience, University of TexasAustinUnited States
- Department of Brain and Cognitive Sciences and McGovern Institute, MITAustinUnited States
| | - Lorenzo A Sadun
- Department of Mathematics and Neuroscience, The University of TexasAustinUnited States
| | - Ila R Fiete
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Brain and Cognitive Sciences and McGovern Institute, MITAustinUnited States
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Neuroscience, University of TexasAustinUnited States
- Department of Mathematics and Neuroscience, The University of TexasAustinUnited States
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49
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Experience-dependent contextual codes in the hippocampus. Nat Neurosci 2021; 24:705-714. [PMID: 33753945 PMCID: PMC8893323 DOI: 10.1038/s41593-021-00816-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/10/2021] [Indexed: 01/30/2023]
Abstract
The hippocampus contains neural representations capable of supporting declarative memory. Hippocampal place cells are one such representation, firing in one or few locations in a given environment. Between environments, place cell firing fields remap (turning on/off or moving to a new location) to provide a population-wide code for distinct contexts. However, the manner by which contextual features combine to drive hippocampal remapping remains a matter of debate. Using large-scale in vivo two-photon intracellular calcium recordings in mice during virtual navigation, we show that remapping in the hippocampal region CA1 is driven by prior experience regarding the frequency of certain contexts and that remapping approximates an optimal estimate of the identity of the current context. A simple associative-learning mechanism reproduces these results. Together, our findings demonstrate that place cell remapping allows an animal to simultaneously identify its physical location and optimally estimate the identity of the environment.
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50
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Santos-Pata D, Amil AF, Raikov IG, Rennó-Costa C, Mura A, Soltesz I, Verschure PF. Entorhinal mismatch: A model of self-supervised learning in the hippocampus. iScience 2021; 24:102364. [PMID: 33997671 PMCID: PMC8091892 DOI: 10.1016/j.isci.2021.102364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/30/2021] [Accepted: 03/24/2021] [Indexed: 12/03/2022] Open
Abstract
The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals "countercurrent" to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.
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Affiliation(s)
- Diogo Santos-Pata
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
| | - Adrián F. Amil
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | | | - César Rennó-Costa
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Anna Mura
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Paul F.M.J. Verschure
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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