1
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Thompson JC, Parkinson C. Interactions between neural representations of the social and spatial environment. Philos Trans R Soc Lond B Biol Sci 2024; 379:20220522. [PMID: 39230453 PMCID: PMC11449203 DOI: 10.1098/rstb.2022.0522] [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] [Received: 12/22/2023] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 09/05/2024] Open
Abstract
Even in our highly interconnected modern world, geographic factors play an important role in human social connections. Similarly, social relationships influence how and where we travel, and how we think about our spatial world. Here, we review the growing body of neuroscience research that is revealing multiple interactions between social and spatial processes in both humans and non-human animals. We review research on the cognitive and neural representation of spatial and social information, and highlight recent findings suggesting that underlying mechanisms might be common to both. We discuss how spatial factors can influence social behaviour, and how social concepts modify representations of space. In so doing, this review elucidates not only how neural representations of social and spatial information interact but also similarities in how the brain represents and operates on analogous information about its social and spatial surroundings.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.
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Affiliation(s)
- James C. Thompson
- Department of Psychology, and Center for Adaptive Systems of Brain-Body Interactions, George Mason University, MS3F5 4400 University Drive, Fairfax, VA22030, USA
| | - Carolyn Parkinson
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
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2
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Basu J, Nagel K. Neural circuits for goal-directed navigation across species. Trends Neurosci 2024:S0166-2236(24)00177-2. [PMID: 39393938 DOI: 10.1016/j.tins.2024.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 08/26/2024] [Accepted: 09/17/2024] [Indexed: 10/13/2024]
Abstract
Across species, navigation is crucial for finding both resources and shelter. In vertebrates, the hippocampus supports memory-guided goal-directed navigation, whereas in arthropods the central complex supports similar functions. A growing literature is revealing similarities and differences in the organization and function of these brain regions. We review current knowledge about how each structure supports goal-directed navigation by building internal representations of the position or orientation of an animal in space, and of the location or direction of potential goals. We describe input pathways to each structure - medial and lateral entorhinal cortex in vertebrates, and columnar and tangential neurons in insects - that primarily encode spatial and non-spatial information, respectively. Finally, we highlight similarities and differences in spatial encoding across clades and suggest experimental approaches to compare coding principles and behavioral capabilities across species. Such a comparative approach can provide new insights into the neural basis of spatial navigation and neural computation.
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Affiliation(s)
- Jayeeta Basu
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
| | - Katherine Nagel
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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3
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Shimbo A, Takahashi YK, Langdon AJ, Stalnaker TA, Schoenbaum G. Cracking and Packing Information about the Features of Expected Rewards in the Orbitofrontal Cortex. J Neurosci 2024; 44:e0714242024. [PMID: 39122558 PMCID: PMC11376335 DOI: 10.1523/jneurosci.0714-24.2024] [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: 04/16/2024] [Revised: 07/29/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
The orbitofrontal cortex (OFC) is crucial for tracking various aspects of expected outcomes, thereby helping to guide choices and support learning. Our previous study showed that the effects of reward timing and size on the activity of single units in OFC were dissociable when these attributes were manipulated independently ( Roesch et al., 2006). However, in real-life decision-making scenarios, outcome features often change simultaneously, so here we investigated how OFC neurons in male rats integrate information about the timing and identity (flavor) of reward and respond to changes in these features, according to whether they were changed simultaneously or separately. We found that a substantial number of OFC neurons fired differentially to immediate versus delayed reward and to the different reward flavors. However, contrary to the previous study, selectivity for timing was strongly correlated with selectivity for identity. Taken together with the previous research, these results suggest that when reward features are correlated, OFC tends to "pack" them into unitary constructs, whereas when they are independent, OFC tends to "crack" them into separate constructs. Furthermore, we found that when both reward timing and flavor were changed, reward-responsive OFC neurons showed unique activity patterns preceding and during the omission of an expected reward. Interestingly, this OFC activity is similar and slightly preceded the ventral tegmental area dopamine (VTA DA) activity observed in a previous study ( Takahashi et al., 2023), consistent with the role of OFC in providing predictive information to VTA DA neurons.
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Affiliation(s)
- Akihiro Shimbo
- National Institute on Drug Abuse Intramural Research Program, Cellular Neurobiology Research Branch, Behavioral Neurophysiology Research Section, Baltimore, Maryland 21224
| | - Yuji K Takahashi
- National Institute on Drug Abuse Intramural Research Program, Cellular Neurobiology Research Branch, Behavioral Neurophysiology Research Section, Baltimore, Maryland 21224
| | - Angela J Langdon
- National Institute on Drug Abuse Intramural Research Program, Cellular Neurobiology Research Branch, Behavioral Neurophysiology Research Section, Baltimore, Maryland 21224
| | - Thomas A Stalnaker
- National Institute on Drug Abuse Intramural Research Program, Cellular Neurobiology Research Branch, Behavioral Neurophysiology Research Section, Baltimore, Maryland 21224
| | - Geoffrey Schoenbaum
- National Institute on Drug Abuse Intramural Research Program, Cellular Neurobiology Research Branch, Behavioral Neurophysiology Research Section, Baltimore, Maryland 21224
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4
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Jun H, Lee JY, Bleza NR, Ichii A, Donohue JD, Igarashi KM. Prefrontal and lateral entorhinal neurons co-dependently learn item-outcome rules. Nature 2024; 633:864-871. [PMID: 39169188 DOI: 10.1038/s41586-024-07868-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 07/23/2024] [Indexed: 08/23/2024]
Abstract
The ability to learn novel items depends on brain functions that store information about items classified by their associated meanings and outcomes1-4, but the underlying neural circuit mechanisms of this process remain poorly understood. Here we show that deep layers of the lateral entorhinal cortex (LEC) contain two groups of 'item-outcome neurons': one developing activity for rewarded items during learning, and another for punished items. As mice learned an olfactory item-outcome association, we found that the neuronal population of LEC layers 5/6 (LECL5/6) formed an internal map of pre-learned and novel items, classified into dichotomic rewarded versus punished groups. Neurons in the medial prefrontal cortex (mPFC), which form a bidirectional loop circuit with LECL5/6, developed an equivalent item-outcome rule map during learning. When LECL5/6 neurons were optogenetically inhibited, tangled mPFC representations of novel items failed to split into rewarded versus punished groups, impairing new learning by mice. Conversely, when mPFC neurons were inhibited, LECL5/6 representations of individual items were held completely separate, disrupting both learning and retrieval of associations. These results suggest that LECL5/6 neurons and mPFC neurons co-dependently encode item memory as a map of associated outcome rules.
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Affiliation(s)
- Heechul Jun
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Jason Y Lee
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Nicholas R Bleza
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Ayana Ichii
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Jordan D Donohue
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Kei M Igarashi
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA, USA.
- Department of Biomedical Engineering, Samueli School of Engineering, University of California Irvine, Irvine, CA, USA.
- Center for Neural Circuit Mapping, School of Medicine, University of California Irvine, Irvine, CA, USA.
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA.
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA, USA.
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5
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Ohtake M, Abe K, Hasegawa M, Itokazu T, Selvakumar V, Matunis A, Stacy E, Froebrich E, Huynh N, Lee H, Kambe Y, Yamamoto T, Sato TK, Sato TR. Encoding of self-initiated actions in axon terminals of the mesocortical pathway. NEUROPHOTONICS 2024; 11:033408. [PMID: 38726349 PMCID: PMC11080647 DOI: 10.1117/1.nph.11.3.033408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/12/2024]
Abstract
Significance The initiation of goal-directed actions is a complex process involving the medial prefrontal cortex and dopaminergic inputs through the mesocortical pathway. However, it is unclear what information the mesocortical pathway conveys and how it impacts action initiation. In this study, we unveiled the indispensable role of mesocortical axon terminals in encoding the execution of movements in self-initiated actions. Aim To investigate the role of mesocortical axon terminals in encoding the execution of movements in self-initiated actions. Approach We designed a lever-press task in which mice internally determine the timing of the press, receiving a larger reward for longer waiting periods. Results Our study revealed that self-initiated actions depend on dopaminergic signaling mediated by D2 receptors, whereas sensory-triggered lever-press actions do not involve D2 signaling. Microprism-mediated two-photon calcium imaging further demonstrated ramping activity in mesocortical axon terminals approximately 0.5 s before the self-initiated lever press. Remarkably, the ramping patterns remained consistent whether the mice responded to cues immediately for a smaller reward or held their response for a larger reward. Conclusions We conclude that mesocortical dopamine axon terminals encode the timing of self-initiated actions, shedding light on a crucial aspect of the intricate neural mechanisms governing goal-directed behavior.
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Affiliation(s)
- Makoto Ohtake
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
- Yokohama City University, Department of Neurosurgery, Yokohama, Japan
| | - Kenta Abe
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
| | - Masashi Hasegawa
- Rutgers, The State University of New Jersey, Robert Wood Johnson Medical School, Center for Advanced Biotechnology and Medicine, Department of Neuroscience and Cell Biology, Piscataway, New Jersey, United States
| | - Takahide Itokazu
- Osaka University, Department of Neuro-Medical Science, Osaka, Japan
| | - Vihashini Selvakumar
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
| | - Ashley Matunis
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
- College of Charleston, Department of Biology, Charleston, South Carolina, United States
| | - Emma Stacy
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
- College of Charleston, Department of Biology, Charleston, South Carolina, United States
| | - Emily Froebrich
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
- College of Charleston, Department of Biology, Charleston, South Carolina, United States
| | - Nathan Huynh
- Kagoshima University, Department of Pharmacology, Kagoshima, Japan
| | - Haesuk Lee
- Kagoshima University, Department of Pharmacology, Kagoshima, Japan
| | - Yuki Kambe
- Kagoshima University, Department of Pharmacology, Kagoshima, Japan
| | - Tetsuya Yamamoto
- Yokohama City University, Department of Neurosurgery, Yokohama, Japan
| | - Tatsuo K. Sato
- Kagoshima University, Department of Pharmacology, Kagoshima, Japan
- FOREST, Japan Science and Technology Agency, Saitama, Japan
| | - Takashi R. Sato
- Medical University of South Carolina, Department of Neuroscience, Charleston, South Carolina, United States
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6
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Kaltenecker D, Al-Maskari R, Negwer M, Hoeher L, Kofler F, Zhao S, Todorov M, Rong Z, Paetzold JC, Wiestler B, Piraud M, Rueckert D, Geppert J, Morigny P, Rohm M, Menze BH, Herzig S, Berriel Diaz M, Ertürk A. Virtual reality-empowered deep-learning analysis of brain cells. Nat Methods 2024; 21:1306-1315. [PMID: 38649742 PMCID: PMC11239522 DOI: 10.1038/s41592-024-02245-2] [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: 06/03/2022] [Accepted: 03/12/2024] [Indexed: 04/25/2024]
Abstract
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
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Affiliation(s)
- Doris Kaltenecker
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Rami Al-Maskari
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
| | - Moritz Negwer
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Luciano Hoeher
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Florian Kofler
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Shan Zhao
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Mihail Todorov
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Zhouyi Rong
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Johannes Christian Paetzold
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Piraud
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Geppert
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Pauline Morigny
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Maria Rohm
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Bjoern H Menze
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Department for Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Stephan Herzig
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Chair Molecular Metabolic Control, TU Munich, Munich, Germany
| | - Mauricio Berriel Diaz
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany.
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
- Deep Piction, Munich, Germany.
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7
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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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8
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Tang H, Bartolo-Orozco R, Averbeck BB. Ventral frontostriatal circuitry mediates the computation of reinforcement from symbolic gains and losses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.587097. [PMID: 38617219 PMCID: PMC11014508 DOI: 10.1101/2024.04.03.587097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Reinforcement learning (RL), particularly in primates, is often driven by symbolic outcomes. However, it is usually studied with primary reinforcers. To examine the neural mechanisms underlying learning from symbolic outcomes, we trained monkeys on a task in which they learned to choose options that led to gains of tokens and avoid choosing options that led to losses of tokens. We then recorded simultaneously from the orbitofrontal cortex (OFC), ventral striatum (VS), amygdala (AMY), and the mediodorsal thalamus (MDt). We found that the OFC played a dominant role in coding token outcomes and token prediction errors. The other areas contributed complementary functions with the VS coding appetitive outcomes and the AMY coding the salience of outcomes. The MDt coded actions and relayed information about tokens between the OFC and VS. Thus, OFC leads the process of symbolic reinforcement learning in the ventral frontostriatal circuitry.
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9
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Qiu Y, Li H, Liao J, Chen K, Wu X, Liu B, Huang R. Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex. Commun Biol 2024; 7:517. [PMID: 38693344 PMCID: PMC11063219 DOI: 10.1038/s42003-024-06214-5] [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: 06/09/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.
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Affiliation(s)
- Yidan Qiu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Huakang Li
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jiajun Liao
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Kemeng Chen
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Xiaoyan Wu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Bingyi Liu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Ruiwang Huang
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China.
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10
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Issa JB, Radvansky BA, Xuan F, Dombeck DA. Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward. Nat Neurosci 2024; 27:536-546. [PMID: 38272968 PMCID: PMC11097142 DOI: 10.1038/s41593-023-01557-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
During goal-directed navigation, 'what' information, describing the experiences occurring in periods surrounding a reward, can be combined with spatial 'where' information to guide behavior and form episodic memories. This integrative process likely occurs in the hippocampus, which receives spatial information from the medial entorhinal cortex; however, the source of the 'what' information is largely unknown. Here, we show that mouse lateral entorhinal cortex (LEC) represents key experiential epochs during reward-based navigation tasks. We discover separate populations of neurons that signal goal approach and goal departure and a third population signaling reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning the new reward location. Therefore, the LEC contains a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the medial entorhinal cortex.
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Affiliation(s)
- John B Issa
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Brad A Radvansky
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Feng Xuan
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL, USA.
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11
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Ahuja A, Rodriguez NY, Ashok AK, Serre T, Desrochers T, Sheinberg D. Monkeys engage in visual simulation to solve complex problems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.21.581495. [PMID: 38464308 PMCID: PMC10925096 DOI: 10.1101/2024.02.21.581495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Visual simulation - i.e., using internal reconstructions of the world to experience potential future versions of events that are not currently happening - is among the most sophisticated capacities of the human mind. But is this ability in fact uniquely human? To answer this question, we tested monkeys on a series of experiments involving the 'Planko' game, which we have previously used to evoke visual simulation in human participants. We found that monkeys were able to successfully play the game using a simulation strategy, predicting the trajectory of a ball through a field of planks while demonstrating a level of accuracy and behavioral signatures comparable to humans. Computational analyses further revealed that the monkeys' strategy while playing Planko aligned with a recurrent neural network (RNN) that approached the task using a spontaneously learned simulation strategy. Finally, we carried out awake functional magnetic resonance imaging while monkeys played Planko. We found activity in motion-sensitive regions of the monkey brain during hypothesized simulation periods, even without any perceived visual motion cues. This neural result closely mirrors previous findings from human research, suggesting a shared mechanism of visual simulation across species. In all, these findings challenge traditional views of animal cognition, proposing that nonhuman primates possess a complex cognitive landscape, capable of invoking imaginative and predictive mental experiences to solve complex everyday problems.
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Affiliation(s)
- Aarit Ahuja
- Department of Neuroscience, Brown University, Providence, RI, USA
- Exponent, Natick, MA, USA
| | | | - Alekh Karkada Ashok
- Department of Cognitive, Linguistic, and Psychological Science, Brown University, Providence, RI, USA
| | - Thomas Serre
- Department of Cognitive, Linguistic, and Psychological Science, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Theresa Desrochers
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, USA
| | - David Sheinberg
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
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12
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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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13
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Guidera JA, Gramling DP, Comrie AE, Joshi A, Denovellis EL, Lee KH, Zhou J, Thompson P, Hernandez J, Yorita A, Haque R, Kirst C, Frank LM. Regional specialization manifests in the reliability of neural population codes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.576941. [PMID: 38328245 PMCID: PMC10849741 DOI: 10.1101/2024.01.25.576941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The brain has the remarkable ability to learn and guide the performance of complex tasks. Decades of lesion studies suggest that different brain regions perform specialized functions in support of complex behaviors1-3. Yet recent large-scale studies of neural activity reveal similar patterns of activity and encoding distributed widely throughout the brain4-6. How these distributed patterns of activity and encoding are compatible with regional specialization of brain function remains unclear. Two frontal brain regions, the dorsal medial prefrontal cortex (dmPFC) and orbitofrontal cortex (OFC), are a paradigm of this conundrum. In the setting complex behaviors, the dmPFC is necessary for choosing optimal actions2,7,8, whereas the OFC is necessary for waiting for3,9 and learning from2,7,9-12 the outcomes of those actions. Yet both dmPFC and OFC encode both choice- and outcome-related quantities13-20. Here we show that while ensembles of neurons in the dmPFC and OFC of rats encode similar elements of a cognitive task with similar patterns of activity, the two regions differ in when that coding is consistent across trials ("reliable"). In line with the known critical functions of each region, dmPFC activity is more reliable when animals are making choices and less reliable preceding outcomes, whereas OFC activity shows the opposite pattern. Our findings identify the dynamic reliability of neural population codes as a mechanism whereby different brain regions may support distinct cognitive functions despite exhibiting similar patterns of activity and encoding similar quantities.
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Affiliation(s)
- Jennifer A. Guidera
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, 94158, USA and University of California, Berkeley; Berkely, 94720, USA
- Medical Scientist Training Program, University of California, San Francisco; San Francisco, 94158, USA
| | - Daniel P. Gramling
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
| | - Alison E. Comrie
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
| | - Abhilasha Joshi
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Eric L. Denovellis
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Kyu Hyun Lee
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Jenny Zhou
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Paige Thompson
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Jose Hernandez
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Allison Yorita
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Razi Haque
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Christoph Kirst
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Department of Anatomy, University of California, San Francisco; San Francisco, 94158, USA
| | - Loren M. Frank
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, 94158, USA and University of California, Berkeley; Berkely, 94720, USA
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
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14
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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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15
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Braine A, Georges F. Emotion in action: When emotions meet motor circuits. Neurosci Biobehav Rev 2023; 155:105475. [PMID: 37996047 DOI: 10.1016/j.neubiorev.2023.105475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/15/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023]
Abstract
The brain is a remarkably complex organ responsible for a wide range of functions, including the modulation of emotional states and movement. Neuronal circuits are believed to play a crucial role in integrating sensory, cognitive, and emotional information to ultimately guide motor behavior. Over the years, numerous studies employing diverse techniques such as electrophysiology, imaging, and optogenetics have revealed a complex network of neural circuits involved in the regulation of emotional or motor processes. Emotions can exert a substantial influence on motor performance, encompassing both everyday activities and pathological conditions. The aim of this review is to explore how emotional states can shape movements by connecting the neural circuits for emotional processing to motor neural circuits. We first provide a comprehensive overview of the impact of different emotional states on motor control in humans and rodents. In line with behavioral studies, we set out to identify emotion-related structures capable of modulating motor output, behaviorally and anatomically. Neuronal circuits involved in emotional processing are extensively connected to the motor system. These circuits can drive emotional behavior, essential for survival, but can also continuously shape ongoing movement. In summary, the investigation of the intricate relationship between emotion and movement offers valuable insights into human behavior, including opportunities to enhance performance, and holds promise for improving mental and physical health. This review integrates findings from multiple scientific approaches, including anatomical tracing, circuit-based dissection, and behavioral studies, conducted in both animal and human subjects. By incorporating these different methodologies, we aim to present a comprehensive overview of the current understanding of the emotional modulation of movement in both physiological and pathological conditions.
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Affiliation(s)
- Anaelle Braine
- Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
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16
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Abstract
Knowing where you are and where you go is a prerequisite for planning a goal-directed journey. The discovery of spatially tuned neurons in the hippocampus and parahippocampal cortices provides a mechanism by which the brain pinpoints an animal’s own position in an environment. By contrast, how the brain encodes a remote navigational goal remained largely obscure until recently. In this review, we discuss algorithmic challenges and requirements for the brain to form a representation of a remote navigational goal at which an animal is not present. We then highlight a line of evidence that neurons in the orbitofrontal cortex (OFC) represent a goal location persistently while an animal navigates to this destination. Finally, we propose a new perspective of navigation research opened by this recently reported brain’s goal map.
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Affiliation(s)
- Raunak Basu
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Hiroshi T. Ito
- Max Planck Institute for Brain Research, Frankfurt am Main 60438, Germany
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17
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Esparza J, Sebastián ER, de la Prida LM. From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable. Curr Opin Neurobiol 2023; 83:102800. [PMID: 37898015 DOI: 10.1016/j.conb.2023.102800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/30/2023]
Abstract
The study of the hippocampal code is gaining momentum. While the physiological approach targets the contribution of individual cells as determined by genetic, biophysical and circuit factors, the field pushes for a population dynamic approach that considers the representation of behavioural variables by a large number of neurons. In this alternative framework, neuronal activity is projected into low-dimensional manifolds. These manifolds can reveal the structure of population representations, but their physiological interpretation is challenging. Here, we review the recent literature and propose that integrating information regarding behavioral traits, local field potential oscillations and cell-type-specificity into neural manifolds offers strategies to make them interpretable at the physiological level.
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18
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Lee CH, Park YK, Lee K. Recent strategies for neural dynamics observation at a larger scale and wider scope. Biosens Bioelectron 2023; 240:115638. [PMID: 37647685 DOI: 10.1016/j.bios.2023.115638] [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: 04/14/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.
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Affiliation(s)
- Chang Hak Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Young Kwon Park
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Kwang Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea.
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19
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Issa JB, Radvansky BA, Xuan F, Dombeck DA. Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561557. [PMID: 37873482 PMCID: PMC10592707 DOI: 10.1101/2023.10.09.561557] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
During goal-directed navigation, "what" information, which describes the experiences occurring in periods surrounding a reward, can be combined with spatial "where" information to guide behavior and form episodic memories1,2. This integrative process is thought to occur in the hippocampus3, which receives spatial information from the medial entorhinal cortex (MEC)4; however, the source of the "what" information and how it is represented is largely unknown. Here, by establishing a novel imaging method, we show that the lateral entorhinal cortex (LEC) of mice represents key experiential epochs during a reward-based navigation task. We discover a population of neurons that signals goal approach and a separate population of neurons that signals goal departure. A third population of neurons signals reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning of the new reward location. Together, these results indicate the LEC provides a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the MEC. Such parallel representations are well-suited for generating episodic memories of rewarding experiences and guiding flexible and efficient goal-directed navigation5-7.
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Affiliation(s)
- John B. Issa
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Brad A. Radvansky
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Feng Xuan
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Daniel A. Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
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20
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Maisson DJN, Cervera RL, Voloh B, Conover I, Zambre M, Zimmermann J, Hayden BY. Widespread coding of navigational variables in prefrontal cortex. Curr Biol 2023; 33:3478-3488.e3. [PMID: 37541250 PMCID: PMC10984098 DOI: 10.1016/j.cub.2023.07.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/01/2023] [Accepted: 07/13/2023] [Indexed: 08/06/2023]
Abstract
To navigate effectively, we must represent information about our location in the environment. Traditional research highlights the role of the hippocampal complex in this process. Spurred by recent research highlighting the widespread cortical encoding of cognitive and motor variables previously thought to have localized function, we hypothesized that navigational variables would be likewise encoded widely, especially in the prefrontal cortex, which is associated with volitional behavior. We recorded neural activity from six prefrontal regions while macaques performed a foraging task in an open enclosure. In all regions, we found strong encoding of allocentric position, allocentric head direction, boundary distance, and linear and angular velocity. These encodings were not accounted for by distance, time to reward, or motor factors. The strength of coding of all variables increased along a ventral-to-dorsal gradient. Together, these results argue that encoding of navigational variables is not localized to the hippocampus and support the hypothesis that navigation is continuous with other forms of flexible cognition in the service of action.
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Affiliation(s)
- David J-N Maisson
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Roberto Lopez Cervera
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Voloh
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Indirah Conover
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mrunal Zambre
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
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21
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Bernáez Timón L, Ekelmans P, Kraynyukova N, Rose T, Busse L, Tchumatchenko T. How to incorporate biological insights into network models and why it matters. J Physiol 2023; 601:3037-3053. [PMID: 36069408 DOI: 10.1113/jp282755] [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: 04/22/2022] [Accepted: 08/24/2022] [Indexed: 11/08/2022] Open
Abstract
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
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Affiliation(s)
- Laura Bernáez Timón
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
| | - Pierre Ekelmans
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Nataliya Kraynyukova
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Tobias Rose
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
| | - Tatjana Tchumatchenko
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
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22
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Xie B, Zhen Z, Guo O, Li H, Guo M, Zhen J. Progress on the hippocampal circuits and functions based on sharp wave ripples. Brain Res Bull 2023:110695. [PMID: 37353037 DOI: 10.1016/j.brainresbull.2023.110695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/18/2023] [Accepted: 06/20/2023] [Indexed: 06/25/2023]
Abstract
Sharp wave ripples (SWRs) are high-frequency synchronization events generated by hippocampal neuronal circuits during various forms of learning and reactivated during memory consolidation and recall. There is mounting evidence that SWRs are essential for storing spatial and social memories in rodents and short-term episodic memories in humans. Sharp wave ripples originate mainly from the hippocampal CA3 and subiculum, and can be transmitted to modulate neuronal activity in cortical and subcortical regions for long-term memory consolidation and behavioral guidance. Different hippocampal subregions have distinct functions in learning and memory. For instance, the dorsal CA1 is critical for spatial navigation, episodic memory, and learning, while the ventral CA1 and dorsal CA2 may work cooperatively to store and consolidate social memories. Here, we summarize recent studies demonstrating that SWRs are essential for the consolidation of spatial, episodic, and social memories in various hippocampal-cortical pathways, and review evidence that SWR dysregulation contributes to cognitive impairments in neurodegenerative and neurodevelopmental diseases.
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Affiliation(s)
- Boxu Xie
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhihang Zhen
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ouyang Guo
- Department of Biology, Boston University, Boston, MA, United States
| | - Heming Li
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Moran Guo
- Neurological Laboratory of Hebei Province, Shijiazhuang, China
| | - Junli Zhen
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China; Neurological Laboratory of Hebei Province, Shijiazhuang, China.
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23
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Stress disrupts insight-driven mnemonic reconfiguration in the medial temporal lobe. Neuroimage 2023; 265:119804. [PMID: 36503160 PMCID: PMC9878442 DOI: 10.1016/j.neuroimage.2022.119804] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/13/2022] Open
Abstract
Memories are not stored in isolation. Insight into the relationship of initially unrelated events may trigger a flexible reconfiguration of the mnemonic representation of these events. Such representational changes allow the integration of events into coherent episodes and help to build up-to-date-models of the world around us. This process is, however, frequently impaired in stress-related mental disorders resulting in symptoms such as fragmented memories in PTSD. Here, we combined a real life-like narrative-insight task, in which participants learned how initially separate events are linked, with fMRI-based representational similarity analysis to test if and how acute stress interferes with the insight-driven reconfiguration of memories. Our results showed that stress reduced the activity of medial temporal and prefrontal areas when participants gained insight into the link between events. Moreover, stress abolished the insight-related increase in representational dissimilarity for linked events in the anterior part of the hippocampus as well as its association with measures of subsequent memory that we observed in non-stressed controls. However, memory performance, as assessed in a forced-choice recognition test, was even enhanced in the stress group. Our findings suggest that acute stress impedes the neural integration of events into coherent episodes but promotes long-term memory for these integrated narratives and may thus have implications for understanding memory distortions in stress-related mental disorders.
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24
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Rosado OG, Amil AF, Freire IT, Verschure PFMJ. Drive competition underlies effective allostatic orchestration. Front Robot AI 2022; 9:1052998. [PMID: 36530500 PMCID: PMC9755511 DOI: 10.3389/frobt.2022.1052998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 11/17/2022] [Indexed: 09/10/2024] Open
Abstract
Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their internal states within a desirable range, the problem of orchestrating different homeostatic systems has not been fully explained yet. In the present paper, we argue that attractor dynamics emerge from the competitive relation of internal drives, resulting in the effective regulation of adaptive behaviors. To test this hypothesis, we develop a biologically-grounded attractor model of allostatic orchestration that is embedded into a synthetic agent. Results show that the resultant neural mass model allows the agent to reproduce the navigational patterns of a rodent in an open field. Moreover, when exploring the robustness of our model in a dynamically changing environment, the synthetic agent pursues the stability of the self, being its internal states dependent on environmental opportunities to satisfy its needs. Finally, we elaborate on the benefits of resetting the model's dynamics after drive-completion behaviors. Altogether, our studies suggest that the neural mass allostatic model adequately reproduces self-regulatory dynamics while overcoming the limitations of previous models.
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Affiliation(s)
- Oscar Guerrero Rosado
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | | | | | - Paul F. M. J. Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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25
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Uncovering the Hippocampal Mechanisms Underpinning Spatial Learning and Flexible Navigation. J Neurosci 2022; 42:8915-8917. [PMID: 36450594 PMCID: PMC9732818 DOI: 10.1523/jneurosci.1400-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 01/05/2023] Open
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Maisson DJN, Wikenheiser A, Noel JPG, Keinath AT. Making Sense of the Multiplicity and Dynamics of Navigational Codes in the Brain. J Neurosci 2022; 42:8450-8459. [PMID: 36351831 PMCID: PMC9665915 DOI: 10.1523/jneurosci.1124-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
Since the discovery of conspicuously spatially tuned neurons in the hippocampal formation over 50 years ago, characterizing which, where, and how neurons encode navigationally relevant variables has been a major thrust of navigational neuroscience. While much of this effort has centered on the hippocampal formation and functionally-adjacent structures, recent work suggests that spatial codes, in some form or another, can be found throughout the brain, even in areas traditionally associated with sensation, movement, and executive function. In this review, we highlight these unexpected results, draw insights from comparison of these codes across contexts, regions, and species, and finally suggest an avenue for future work to make sense of these diverse and dynamic navigational codes.
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Affiliation(s)
- David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455
| | - Andrew Wikenheiser
- Department of Psychology, University of California, Los Angeles, California 90024
| | - Jean-Paul G Noel
- Center for Neural Science, New York University, New York, New York 10003
| | - Alexandra T Keinath
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Verdun H3A 0G4, Quebec Canada
- Department of Psychology, University of IL Chicago, Chicago, Illinois 60607
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27
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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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28
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Chen ZS, Zhang X, Long X, Zhang SJ. Are Grid-Like Representations a Component of All Perception and Cognition? Front Neural Circuits 2022; 16:924016. [PMID: 35911570 PMCID: PMC9329517 DOI: 10.3389/fncir.2022.924016] [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: 04/20/2022] [Accepted: 06/14/2022] [Indexed: 11/24/2022] Open
Abstract
Grid cells or grid-like responses have been reported in the rodent, bat and human brains during various spatial and non-spatial tasks. However, the functions of grid-like representations beyond the classical hippocampal formation remain elusive. Based on accumulating evidence from recent rodent recordings and human fMRI data, we make speculative accounts regarding the mechanisms and functional significance of the sensory cortical grid cells and further make theory-driven predictions. We argue and reason the rationale why grid responses may be universal in the brain for a wide range of perceptual and cognitive tasks that involve locomotion and mental navigation. Computational modeling may provide an alternative and complementary means to investigate the grid code or grid-like map. We hope that the new discussion will lead to experimentally testable hypotheses and drive future experimental data collection.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, Neuroscience Institute, New York University School of Medicine, New York, NY, United States
| | - Xiaohan Zhang
- Department of Psychiatry, Department of Neuroscience and Physiology, Neuroscience Institute, New York University School of Medicine, New York, NY, United States
| | - Xiaoyang Long
- Department of Neurosurgery, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Sheng-Jia Zhang
- Department of Neurosurgery, Xinqiao Hospital, Army Medical University, Chongqing, China
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29
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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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30
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Pfeiffer BE. Spatial Learning Drives Rapid Goal Representation in Hippocampal Ripples without Place Field Accumulation or Goal-Oriented Theta Sequences. J Neurosci 2022; 42:3975-3988. [PMID: 35396328 PMCID: PMC9097771 DOI: 10.1523/jneurosci.2479-21.2022] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 01/05/2023] Open
Abstract
The hippocampus is critical for rapid acquisition of many forms of memory, although the circuit-level mechanisms through which the hippocampus rapidly consolidates novel information are unknown. Here, the activity of large ensembles of hippocampal neurons in adult male Long-Evans rats was monitored across a period of rapid spatial learning to assess how the network changes during the initial phases of memory formation and retrieval. In contrast to several reports, the hippocampal network did not display enhanced representation of the goal location via accumulation of place fields or elevated firing rates at the goal. Rather, population activity rates increased globally as a function of experience. These alterations in activity were mirrored in the power of the theta oscillation and in the quality of theta sequences, without preferential encoding of paths to the learned goal location. In contrast, during brief "offline" pauses in movement, representation of a novel goal location emerged rapidly in ripples, preceding other changes in network activity. These data demonstrate that the hippocampal network can facilitate active navigation without enhanced goal representation during periods of active movement, and further indicate that goal representation in hippocampal ripples before movement onset supports subsequent navigation, possibly through activation of downstream cortical networks.SIGNIFICANCE STATEMENT Understanding the mechanisms through which the networks of the brain rapidly assimilate information and use previously learned knowledge are fundamental areas of focus in neuroscience. In particular, the hippocampal circuit is a critical region for rapid formation and use of spatial memory. In this study, several circuit-level features of hippocampal function were quantified while rats performed a spatial navigation task requiring rapid memory formation and use. During periods of active navigation, a general increase in overall network activity is observed during memory acquisition, which plateaus during memory retrieval periods, without specific enhanced representation of the goal location. During pauses in navigation, rapid representation of the distant goal well emerges before either behavioral improvement or changes in online activity.
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Affiliation(s)
- Brad E Pfeiffer
- Neuroscience Graduate Program, Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390
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31
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Danchin A, Fenton AA. From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine? Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.796413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine.
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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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