1
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Huang Q, Luo H. Shared structure facilitates working memory of multiple sequences. eLife 2024; 12:RP93158. [PMID: 39046319 PMCID: PMC11268885 DOI: 10.7554/elife.93158] [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: 07/25/2024] Open
Abstract
Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.
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Affiliation(s)
- Qiaoli Huang
- School of Psychological and Cognitive Sciences, Peking UniversityBeijingChina
- PKU-IDG/McGovern Institute for Brain Research, Peking UniversityBeijingChina
- Beijing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Huan Luo
- School of Psychological and Cognitive Sciences, Peking UniversityBeijingChina
- PKU-IDG/McGovern Institute for Brain Research, Peking UniversityBeijingChina
- Beijing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
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2
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Huang LW, Torelli F, Chen HL, Bartos M. Context and space coding in mossy cell population activity. Cell Rep 2024; 43:114386. [PMID: 38909362 DOI: 10.1016/j.celrep.2024.114386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 05/07/2024] [Accepted: 06/05/2024] [Indexed: 06/25/2024] Open
Abstract
The dentate gyrus plays a key role in the discrimination of memories by segregating and storing similar episodes. Whether hilar mossy cells, which constitute a major excitatory principal cell type in the mammalian hippocampus, contribute to this decorrelation function has remained largely unclear. Using two-photon calcium imaging of head-fixed mice performing a spatial virtual reality task, we show that mossy cell populations robustly discriminate between familiar and novel environments. The degree of discrimination depends on the extent of visual cue differences between contexts. A context decoder revealed that successful environmental classification is explained mainly by activity difference scores of mossy cells. By decoding mouse position, we reveal that in addition to place cells, the coordinated activity among active mossy cells markedly contributes to the encoding of space. Thus, by decorrelating context information according to the degree of environmental differences, mossy cell populations support pattern separation processes within the dentate gyrus.
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Affiliation(s)
- Li-Wen Huang
- Institute for Physiology I, University of Freiburg, Medical Faculty, 79104 Freiburg, Germany
| | - Federico Torelli
- Institute for Physiology I, University of Freiburg, Medical Faculty, 79104 Freiburg, Germany; University of Freiburg, Faculty of Biology, 79104 Freiburg, Germany
| | - Hung-Ling Chen
- Institute for Physiology I, University of Freiburg, Medical Faculty, 79104 Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, 79104 Freiburg, Germany.
| | - Marlene Bartos
- Institute for Physiology I, University of Freiburg, Medical Faculty, 79104 Freiburg, Germany.
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3
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Luo DD, Giri B, Diba K, Kemere C. Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding. Neural Comput 2024; 36:1449-1475. [PMID: 39028957 DOI: 10.1162/neco_a_01685] [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: 10/30/2023] [Accepted: 03/28/2024] [Indexed: 07/21/2024]
Abstract
Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.
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Affiliation(s)
- Della Daiyi Luo
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, U.S.A.
| | - Bapun Giri
- Department of Anesthesiology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, U.S.A.
| | - Kamran Diba
- Department of Anesthesiology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, U.S.A.
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, U.S.A.
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4
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Courellis HS, Valiante TA, Mamelak AN, Adolphs R, Rutishauser U. Neural dynamics underlying minute-timescale persistent behavior in the human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.16.603717. [PMID: 39071326 PMCID: PMC11275932 DOI: 10.1101/2024.07.16.603717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.
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5
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Ostojic S, Fusi S. Computational role of structure in neural activity and connectivity. Trends Cogn Sci 2024; 28:677-690. [PMID: 38553340 DOI: 10.1016/j.tics.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 07/05/2024]
Abstract
One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.
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Affiliation(s)
- Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005 Paris, France.
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA
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6
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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024; 28:662-676. [PMID: 38658218 DOI: 10.1016/j.tics.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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Affiliation(s)
- Mikael Lundqvist
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonatan Nordmark
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Johan Liljefors
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Pawel Herman
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
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7
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Mizuta K, Sato M. Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions. NEUROPHOTONICS 2024; 11:033406. [PMID: 38464393 PMCID: PMC10923542 DOI: 10.1117/1.nph.11.3.033406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 03/12/2024]
Abstract
Significance The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits. Aim This review aims to summarize and discuss the recent technical advances in multiphoton imaging of hippocampal neural circuits and the accumulated biological knowledge gained through this technology. Approach First, we provide a brief overview of various techniques of multiphoton imaging of the hippocampus and discuss its advantages, drawbacks, and associated key innovations and practices. Then, we review a large body of findings obtained through multiphoton imaging by region (CA1 and dentate gyrus), cell type (pyramidal neurons, inhibitory interneurons, and glial cells), and cellular compartment (dendrite and axon). Results Multiphoton imaging of the hippocampus is primarily performed under head-fixed conditions and can reveal detailed mechanisms of circuit operation owing to its high spatial resolution and specificity. As the hippocampus lies deep below the cortex, its imaging requires elaborate methods. These include imaging cannula implantation, microendoscopy, and the use of long-wavelength light sources. Although many studies have focused on the dorsal CA1 pyramidal cells, studies of other local and inter-areal circuitry elements have also helped provide a more comprehensive picture of the information processing performed by the hippocampal circuits. Imaging of circuit function in mouse models of Alzheimer's disease and other brain disorders such as autism spectrum disorder has also contributed greatly to our understanding of their pathophysiology. Conclusions Multiphoton imaging has revealed much regarding region-, cell-type-, and pathway-specific mechanisms in hippocampal function and dysfunction in health and disease. Future technological advances will allow further illustration of the operating principle of the hippocampal circuits via the large-scale, high-resolution, multimodal, and minimally invasive imaging.
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Affiliation(s)
- Kotaro Mizuta
- RIKEN BDR, Kobe, Japan
- New York University Abu Dhabi, Department of Biology, Abu Dhabi, United Arab Emirates
| | - Masaaki Sato
- Hokkaido University Graduate School of Medicine, Department of Neuropharmacology, Sapporo, Japan
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8
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Chen Y, Zhang H, Cameron M, Sejnowski T. Predictive sequence learning in the hippocampal formation. Neuron 2024:S0896-6273(24)00371-4. [PMID: 38917804 DOI: 10.1016/j.neuron.2024.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/21/2024] [Accepted: 05/22/2024] [Indexed: 06/27/2024]
Abstract
The hippocampus receives sequences of sensory inputs from the cortex during exploration and encodes the sequences with millisecond precision. We developed a predictive autoencoder model of the hippocampus including the trisynaptic and monosynaptic circuits from the entorhinal cortex (EC). CA3 was trained as a self-supervised recurrent neural network to predict its next input. We confirmed that CA3 is predicting ahead by analyzing the spike coupling between simultaneously recorded neurons in the dentate gyrus, CA3, and CA1 of the mouse hippocampus. In the model, CA1 neurons signal prediction errors by comparing CA3 predictions to the next direct EC input. The model exhibits the rapid appearance and slow fading of CA1 place cells and displays replay and phase precession from CA3. The model could be learned in a biologically plausible way with error-encoding neurons. Similarities between the hippocampal and thalamocortical circuits suggest that such computation motif could also underlie self-supervised sequence learning in the cortex.
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Affiliation(s)
- Yusi Chen
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA; Computational Neuroscience Center, University of Washington, Seattle, WA 98195, USA.
| | - Huanqiu Zhang
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Cameron
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Terrence Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA.
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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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Rich PD, Thiberge SY, Scott BB, Guo C, Tervo DGR, Brody CD, Karpova AY, Daw ND, Tank DW. Magnetic voluntary head-fixation in transgenic rats enables lifespan imaging of hippocampal neurons. Nat Commun 2024; 15:4154. [PMID: 38755205 PMCID: PMC11099169 DOI: 10.1038/s41467-024-48505-9] [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: 08/16/2023] [Accepted: 05/01/2024] [Indexed: 05/18/2024] Open
Abstract
The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory are not fully understood. Two-photon calcium imaging allows the activity of individual cells to be followed across long periods, but conventional approaches require head-fixation, which limits the type of behavior that can be studied. We present a magnetic voluntary head-fixation system that provides stable optical access to the brain during complex behavior. Compared to previous systems that used mechanical restraint, there are no moving parts and animals can engage and disengage entirely at will. This system is failsafe, easy for animals to use and reliable enough to allow long-term experiments to be routinely performed. Animals completed hundreds of trials per session of an odor discrimination task that required 2-4 s fixations. Together with a reflectance fluorescence collection scheme that increases two-photon signal and a transgenic Thy1-GCaMP6f rat line, we are able to reliably image the cellular activity in the hippocampus during behavior over long periods (median 6 months), allowing us track the same neurons over a large fraction of animals' lives (up to 19 months).
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Affiliation(s)
- P Dylan Rich
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | | | - Benjamin B Scott
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Caiying Guo
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - D Gowanlock R Tervo
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA
| | - Alla Y Karpova
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nathaniel D Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Bezos Center for Neural Circuit Dynamics, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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11
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity that shapes adaptation. Nat Commun 2024; 15:4084. [PMID: 38744847 PMCID: PMC11094149 DOI: 10.1038/s41467-024-48008-7] [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: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
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Affiliation(s)
- Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Mila, Québec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
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12
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Riveland R, Pouget A. Natural language instructions induce compositional generalization in networks of neurons. Nat Neurosci 2024; 27:988-999. [PMID: 38499855 DOI: 10.1038/s41593-024-01607-5] [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: 05/13/2023] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
A fundamental human cognitive feat is to interpret linguistic instructions in order to perform novel tasks without explicit task experience. Yet, the neural computations that might be used to accomplish this remain poorly understood. We use advances in natural language processing to create a neural model of generalization based on linguistic instructions. Models are trained on a set of common psychophysical tasks, and receive instructions embedded by a pretrained language model. Our best models can perform a previously unseen task with an average performance of 83% correct based solely on linguistic instructions (that is, zero-shot learning). We found that language scaffolds sensorimotor representations such that activity for interrelated tasks shares a common geometry with the semantic representations of instructions, allowing language to cue the proper composition of practiced skills in unseen settings. We show how this model generates a linguistic description of a novel task it has identified using only motor feedback, which can subsequently guide a partner model to perform the task. Our models offer several experimentally testable predictions outlining how linguistic information must be represented to facilitate flexible and general cognition in the human brain.
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Affiliation(s)
- Reidar Riveland
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland.
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
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13
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Fortunato C, Bennasar-Vázquez J, Park J, Chang JC, Miller LE, Dudman JT, Perich MG, Gallego JA. Nonlinear manifolds underlie neural population activity during behaviour. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549575. [PMID: 37503015 PMCID: PMC10370078 DOI: 10.1101/2023.07.18.549575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
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Affiliation(s)
- Cátia Fortunato
- Department of Bioengineering, Imperial College London, London UK
| | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Joanna C. Chang
- Department of Bioengineering, Imperial College London, London UK
| | - Lee E. Miller
- Department of Neurosciences, Northwestern University, Chicago IL, USA
- Department of Biomedical Engineering, Northwestern University, Chicago IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago IL, USA, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T. Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Matthew G. Perich
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada
- Québec Artificial Intelligence Institute (MILA), Montréal, Québec, Canada
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London UK
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14
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Talpir I, Livneh Y. Stereotyped goal-directed manifold dynamics in the insular cortex. Cell Rep 2024; 43:114027. [PMID: 38568813 PMCID: PMC11063631 DOI: 10.1016/j.celrep.2024.114027] [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: 11/10/2023] [Revised: 02/12/2024] [Accepted: 03/15/2024] [Indexed: 04/05/2024] Open
Abstract
The insular cortex is involved in diverse processes, including bodily homeostasis, emotions, and cognition. However, we lack a comprehensive understanding of how it processes information at the level of neuronal populations. We leveraged recent advances in unsupervised machine learning to study insular cortex population activity patterns (i.e., neuronal manifold) in mice performing goal-directed behaviors. We find that the insular cortex activity manifold is remarkably consistent across different animals and under different motivational states. Activity dynamics within the neuronal manifold are highly stereotyped during rewarded trials, enabling robust prediction of single-trial outcomes across different mice and across various natural and artificial motivational states. Comparing goal-directed behavior with self-paced free consumption, we find that the stereotyped activity patterns reflect task-dependent goal-directed reward anticipation, and not licking, taste, or positive valence. These findings reveal a core computation in insular cortex that could explain its involvement in pathologies involving aberrant motivations.
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Affiliation(s)
- Itay Talpir
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yoav Livneh
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
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15
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Qu Y, Wei C, Du P, Che W, Zhang C, Ouyang W, Bian Y, Xu F, Hu B, Du K, Wu H, Liu J, Liu Q. Integration of cognitive tasks into artificial general intelligence test for large models. iScience 2024; 27:109550. [PMID: 38595796 PMCID: PMC11001637 DOI: 10.1016/j.isci.2024.109550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.
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Affiliation(s)
- Youzhi Qu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chen Wei
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Penghui Du
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wenxin Che
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chi Zhang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | | | | | - Feiyang Xu
- iFLYTEK AI Research, Hefei 230088, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing 100871, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China
| | - Jia Liu
- Department of Psychology, Tsinghua University, Beijing 100084, China
| | - Quanying Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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16
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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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17
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Chettih SN, Mackevicius EL, Hale S, Aronov D. Barcoding of episodic memories in the hippocampus of a food-caching bird. Cell 2024; 187:1922-1935.e20. [PMID: 38554707 PMCID: PMC11015962 DOI: 10.1016/j.cell.2024.02.032] [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: 07/18/2023] [Revised: 11/28/2023] [Accepted: 02/23/2024] [Indexed: 04/02/2024]
Abstract
The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees-food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.
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Affiliation(s)
- Selmaan N Chettih
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Emily L Mackevicius
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Basis Research Institute, New York, NY 10027, USA
| | - Stephanie Hale
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
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18
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Losey DM, Hennig JA, Oby ER, Golub MD, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Yu BM, Chase SM. Learning leaves a memory trace in motor cortex. Curr Biol 2024; 34:1519-1531.e4. [PMID: 38531360 PMCID: PMC11097210 DOI: 10.1016/j.cub.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 12/06/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.
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Affiliation(s)
- Darby M Losey
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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19
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Yadav N, Toader A, Rajasethupathy P. Beyond hippocampus: Thalamic and prefrontal contributions to an evolving memory. Neuron 2024; 112:1045-1059. [PMID: 38272026 DOI: 10.1016/j.neuron.2023.12.021] [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: 08/04/2023] [Revised: 11/07/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024]
Abstract
The hippocampus has long been at the center of memory research, and rightfully so. However, with emerging technological capabilities, we can increasingly appreciate memory as a more dynamic and brain-wide process. In this perspective, our goal is to begin developing models to understand the gradual evolution, reorganization, and stabilization of memories across the brain after their initial formation in the hippocampus. By synthesizing studies across the rodent and human literature, we suggest that as memory representations initially form in hippocampus, parallel traces emerge in frontal cortex that cue memory recall, and as they mature, with sustained support initially from limbic then diencephalic then cortical circuits, they become progressively independent of hippocampus and dependent on a mature cortical representation. A key feature of this model is that, as time progresses, memory representations are passed on to distinct circuits with progressively longer time constants, providing the opportunity to filter, forget, update, or reorganize memories in the process of committing to long-term storage.
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Affiliation(s)
- Nakul Yadav
- Laboratory of Neural Dynamics & Cognition, The Rockefeller University, New York, NY, USA
| | - Andrew Toader
- Laboratory of Neural Dynamics & Cognition, The Rockefeller University, New York, NY, USA
| | - Priya Rajasethupathy
- Laboratory of Neural Dynamics & Cognition, The Rockefeller University, New York, NY, USA.
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20
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Noda T, Aschauer DF, Chambers AR, Seiler JPH, Rumpel S. Representational maps in the brain: concepts, approaches, and applications. Front Cell Neurosci 2024; 18:1366200. [PMID: 38584779 PMCID: PMC10995314 DOI: 10.3389/fncel.2024.1366200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.
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Affiliation(s)
- Takahiro Noda
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Dominik F. Aschauer
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Anna R. Chambers
- Department of Otolaryngology – Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
- Eaton Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston, MA, United States
| | - Johannes P.-H. Seiler
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Simon Rumpel
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
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21
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Yuste R, Cossart R, Yaksi E. Neuronal ensembles: Building blocks of neural circuits. Neuron 2024; 112:875-892. [PMID: 38262413 PMCID: PMC10957317 DOI: 10.1016/j.neuron.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/07/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.
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Affiliation(s)
- Rafael Yuste
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA.
| | - Rosa Cossart
- Inserm, INMED, Turing Center for Living Systems Aix-Marseille University, Marseille, France.
| | - Emre Yaksi
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway; Koç University Research Center for Translational Medicine, Koç University School of Medicine, Istanbul, Turkey.
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22
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Secer G, Knierim JJ, Cowan NJ. Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.12.579874. [PMID: 38562699 PMCID: PMC10983875 DOI: 10.1101/2024.02.12.579874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.
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Affiliation(s)
- Gorkem Secer
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - James J Knierim
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Noah J Cowan
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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23
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Puhger K, Crestani AP, Diniz CRF, Wiltgen BJ. The hippocampus contributes to retroactive stimulus associations during trace fear conditioning. iScience 2024; 27:109035. [PMID: 38375237 PMCID: PMC10875141 DOI: 10.1016/j.isci.2024.109035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
Binding events that occur at different times are essential for memory formation. In trace fear conditioning, animals associate a tone and footshock despite no temporal overlap. The hippocampus is thought to mediate this learning by maintaining a memory of the tone until shock occurrence, however, evidence for sustained hippocampal tone representations is lacking. Here, we demonstrate a retrospective role for the hippocampus in trace fear conditioning. Bulk calcium imaging revealed sustained increases in CA1 activity after footshock that were not observed after tone termination. Optogenetic silencing of CA1 immediately after footshock impaired subsequent memory. Additionally, footshock increased the number of sharp-wave ripples compared to baseline during conditioning. Therefore, post-shock hippocampal activity likely supports learning by reactivating and linking latent tone and shock representations. These findings highlight an underappreciated function of post-trial hippocampal activity in enabling retroactive temporal associations during new learning, as opposed to persistent maintenance of stimulus representations.
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Affiliation(s)
- Kyle Puhger
- Department of Psychology, University of California, Davis, 135 Young Hall, 1 Shields Avenue, Davis, CA 95616, USA
- Center for Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA 95618, USA
| | - Ana P. Crestani
- Center for Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA 95618, USA
| | - Cassiano R.A. F. Diniz
- Center for Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA 95618, USA
| | - Brian J. Wiltgen
- Department of Psychology, University of California, Davis, 135 Young Hall, 1 Shields Avenue, Davis, CA 95616, USA
- Center for Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA 95618, USA
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24
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Morrell MC, Nemenman I, Sederberg A. Neural criticality from effective latent variables. eLife 2024; 12:RP89337. [PMID: 38470471 PMCID: PMC10957169 DOI: 10.7554/elife.89337] [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: 03/13/2024] Open
Abstract
Observations of power laws in neural activity data have raised the intriguing notion that brains may operate in a critical state. One example of this critical state is 'avalanche criticality', which has been observed in various systems, including cultured neurons, zebrafish, rodent cortex, and human EEG. More recently, power laws were also observed in neural populations in the mouse under an activity coarse-graining procedure, and they were explained as a consequence of the neural activity being coupled to multiple latent dynamical variables. An intriguing possibility is that avalanche criticality emerges due to a similar mechanism. Here, we determine the conditions under which latent dynamical variables give rise to avalanche criticality. We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases, avalanche criticality is observed without fine-tuning of model parameters. We identify two regimes of avalanches, both critical but differing in the amount of information carried about the latent variable. Our results suggest that avalanche criticality arises in neural systems in which activity is effectively modeled as a population driven by a few dynamical variables and these variables can be inferred from the population activity.
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Affiliation(s)
- Mia C Morrell
- Department of Physics, New York UniversityNew YorkUnited States
| | - Ilya Nemenman
- Department of Physics, Department of Biology, Initiative in Theory and Modeling of Living Systems, Emory UniversityAtlantaUnited States
| | - Audrey Sederberg
- Department of Neuroscience, University of Minnesota Medical SchoolMinneapolisUnited States
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25
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Luo DD, Giri B, Diba K, Kemere C. Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583340. [PMID: 38496669 PMCID: PMC10942371 DOI: 10.1101/2024.03.04.583340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural state repetition from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson Gaussian-process latent variable model (P-GPLVM) (Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the internal state repetition. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally-compressed patterns of activity, such as those found in population burst events (PBEs) during hippocampal sharp-wave ripples, as well as metrics for assessing whether the inferred new latent variables are congruent with a previously learned manifold in the latent space. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain internal neural states are observed to repeat, which is in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, repetition of internal neural states can be estimated for neural activity during PBEs as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.
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Affiliation(s)
- Della Daiyi Luo
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Bapun Giri
- Department of Anesthesiology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Kamran Diba
- Department of Anesthesiology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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26
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Bein O, Davachi L. Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning. J Neurosci 2024; 44:e0627232023. [PMID: 38129134 PMCID: PMC10919070 DOI: 10.1523/jneurosci.0627-23.2023] [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: 04/05/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.
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Affiliation(s)
- Oded Bein
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, New York 10027
- Center for Clinical Research, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
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27
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Bi Z, Li H, Tian L. Top-down generation of low-resolution representations improves visual perception and imagination. Neural Netw 2024; 171:440-456. [PMID: 38150870 DOI: 10.1016/j.neunet.2023.12.030] [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: 03/25/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
Perception or imagination requires top-down signals from high-level cortex to primary visual cortex (V1) to reconstruct or simulate the representations bottom-up stimulated by the seen images. Interestingly, top-down signals in V1 have lower spatial resolution than bottom-up representations. It is unclear why the brain uses low-resolution signals to reconstruct or simulate high-resolution representations. By modeling the top-down pathway of the visual system using the decoder of a variational auto-encoder (VAE), we reveal that low-resolution top-down signals can better reconstruct or simulate the information contained in the sparse activities of V1 simple cells, which facilitates perception and imagination. This advantage of low-resolution generation is related to facilitating high-level cortex to form geometry-respecting representations observed in experiments. Furthermore, we present two findings regarding this phenomenon in the context of AI-generated sketches, a style of drawings made of lines. First, we found that the quality of the generated sketches critically depends on the thickness of the lines in the sketches: thin-line sketches are harder to generate than thick-line sketches. Second, we propose a technique to generate high-quality thin-line sketches: instead of directly using original thin-line sketches, we use blurred sketches to train VAE or GAN (generative adversarial network), and then infer the thin-line sketches from the VAE- or GAN-generated blurred sketches. Collectively, our work suggests that low-resolution top-down generation is a strategy the brain uses to improve visual perception and imagination, which inspires new sketch-generation AI techniques.
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Affiliation(s)
- Zedong Bi
- Lingang Laboratory, Shanghai 200031, China.
| | - Haoran Li
- Department of Physics, Hong Kong Baptist University, Hong Kong, China
| | - Liang Tian
- Department of Physics, Hong Kong Baptist University, Hong Kong, China; Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, China; Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University, Hong Kong, China; State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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28
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Spens E, Burgess N. A generative model of memory construction and consolidation. Nat Hum Behav 2024; 8:526-543. [PMID: 38242925 PMCID: PMC10963272 DOI: 10.1038/s41562-023-01799-z] [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: 05/30/2023] [Accepted: 12/05/2023] [Indexed: 01/21/2024]
Abstract
Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) to (re)create sensory experiences from latent variable representations in entorhinal, medial prefrontal and anterolateral temporal cortices via the hippocampal formation. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for semantic memory, imagination, episodic future thinking, relational inference and schema-based distortions including boundary extension. The model explains how unique sensory and predictable conceptual elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimizing the use of limited hippocampal storage for new and unusual information. Overall, we believe hippocampal replay training generative models provides a comprehensive account of memory construction, imagination and consolidation.
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Affiliation(s)
- Eleanor Spens
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Neil Burgess
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
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29
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Schafer M, Kamilar-Britt P, Sahani V, Bachi K, Schiller D. Neural Trajectories of Conceptually Related Events. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.04.569670. [PMID: 38187737 PMCID: PMC10769183 DOI: 10.1101/2023.12.04.569670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In a series of conceptually related episodes, meaning arises from the link between these events rather than from each event individually. How does the brain keep track of conceptually related sequences of events (i.e., conceptual trajectories)? In a particular kind of conceptual trajectory-a social relationship-meaning arises from a specific sequence of interactions. To test whether such abstract sequences are neurally tracked, we had participants complete a naturalistic narrative-based social interaction game, during functional magnetic resonance imaging. We modeled the simulated relationships as trajectories through an abstract affiliation and power space. In two independent samples, we found evidence of individual social relationships being tracked with unique sequences of hippocampal states. The neural states corresponded to the accumulated trial-to-trial affiliation and power relations between the participant and each character, such that each relationship's history was captured by its own neural trajectory. Each relationship had its own sequence of states, and all relationships were embedded within the same manifold. As such, we show that the hippocampus represents social relationships with ordered sequences of low-dimensional neural patterns. The number of distinct clusters of states on this manifold is also related to social function, as measured by the size of real-world social networks. These results suggest that our evolving relationships with others are represented in trajectory-like neural patterns.
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Affiliation(s)
- Matthew Schafer
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai; New York City, NY
| | - Philip Kamilar-Britt
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York City, NY
| | - Vyoma Sahani
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York City, NY
| | - Keren Bachi
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York City, NY
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai; New York City, NY
| | - Daniela Schiller
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai; New York City, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York City, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York City, NY
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30
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Ma M, Simoes de Souza F, Futia GL, Anderson SR, Riguero J, Tollin D, Gentile-Polese A, Platt JP, Steinke K, Hiratani N, Gibson EA, Restrepo D. Sequential activity of CA1 hippocampal cells constitutes a temporal memory map for associative learning in mice. Curr Biol 2024; 34:841-854.e4. [PMID: 38325376 DOI: 10.1016/j.cub.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
Sequential neural dynamics encoded by time cells play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. We used two-photon Ca2+ imaging of dorsal CA1 (dCA1) neurons in the stratum pyramidale (SP) in head-fixed mice performing a go-no go associative learning task to investigate how odor valence is temporally encoded in this area of the brain. We found that SP cells responded differentially to the rewarded or unrewarded odor. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual SP cells responding differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior. Our findings indicate that sequential activity of SP cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning. VIDEO ABSTRACT.
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Affiliation(s)
- Ming Ma
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Fabio Simoes de Souza
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Center for Mathematics, Computation and Cognition, Federal University of ABC, Sao Bernardo do Campo 09606-045, SP, Brazil
| | - Gregory L Futia
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sean R Anderson
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jose Riguero
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Daniel Tollin
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Arianna Gentile-Polese
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan P Platt
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kira Steinke
- Integrated Physiology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Naoki Hiratani
- Department of Neuroscience, Washington University, St. Louis, MO 63110, USA
| | - Emily A Gibson
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Diego Restrepo
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
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31
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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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32
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Nakai S, Kitanishi T, Mizuseki K. Distinct manifold encoding of navigational information in the subiculum and hippocampus. SCIENCE ADVANCES 2024; 10:eadi4471. [PMID: 38295173 PMCID: PMC10830115 DOI: 10.1126/sciadv.adi4471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/29/2023] [Indexed: 02/02/2024]
Abstract
The subiculum (SUB) plays a crucial role in spatial navigation and encodes navigational information differently from the hippocampal CA1 area. However, the representation of subicular population activity remains unknown. Here, we investigated the neuronal population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The trajectory of population activity in both areas was confined to low-dimensional neural manifolds homoeomorphic to external space. The manifolds conveyed position, speed, and future path information with higher decoding accuracy in the SUB than in the CA1. The manifolds exhibited common geometry across rats and regions for the CA1 and SUB and between tasks in the SUB. During post-task ripples in slow-wave sleep, population activity represented reward locations/events more frequently in the SUB than in CA1. Thus, the CA1 and SUB encode information distinctly into the neural manifolds that underlie navigational information processing during wakefulness and sleep.
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Affiliation(s)
- Shinya Nakai
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
| | - Takuma Kitanishi
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo 153-8902, Japan
- Komaba Institute for Science, The University of Tokyo, Meguro, Tokyo 153-8902, Japan
- PRESTO, Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
| | - Kenji Mizuseki
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan
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33
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Chen D, Axmacher N, Wang L. Grid codes underlie multiple cognitive maps in the human brain. Prog Neurobiol 2024; 233:102569. [PMID: 38232782 DOI: 10.1016/j.pneurobio.2024.102569] [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: 11/06/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes-empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.
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Affiliation(s)
- Dong Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, 44801, Bochum, Germany
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China.
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34
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Kabrel M, Tulver K, Aru J. The journey within: mental navigation as a novel framework for understanding psychotherapeutic transformation. BMC Psychiatry 2024; 24:91. [PMID: 38302927 PMCID: PMC10835954 DOI: 10.1186/s12888-024-05522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Despite the demonstrated efficacy of psychotherapy, the precise mechanisms that drive therapeutic transformations have posed a challenge and still remain unresolved. Here, we suggest a potential solution to this problem by introducing a framework based on the concept of mental navigation. It refers to our ability to navigate our cognitive space of thoughts, ideas, concepts, and memories, similar to how we navigate physical space. We start by analyzing the neural, cognitive, and experiential constituents intrinsic to mental navigation. Subsequently, we posit that the metaphoric spatial language we employ to articulate introspective experiences (e.g., "unexplored territory" or "going in circles") serves as a robust marker of mental navigation. METHODS Using large text corpora, we compared the utilization of spatial language between transcripts of psychotherapy sessions (≈ 12 M. words), casual everyday conversations (≈ 12 M. words), and fictional dialogues in movies (≈ 14 M. words). We also examined 110 psychotherapy transcripts qualitatively to discern patterns and dynamics associated with mental navigation. RESULTS We found a notable increase in the utilization of spatial metaphors during psychotherapy compared to casual everyday dialogues (U = 192.0, p = .001, d = 0.549) and fictional conversations (U = 211, p < .001, d = 0.792). In turn, analyzing the usage of non-spatial metaphors, we did not find significant differences between the three datasets (H = 0.682, p = 0.710). The qualitative analysis highlighted specific examples of mental navigation at play. CONCLUSION Mental navigation might underlie the psychotherapy process and serve as a robust framework for understanding the transformative changes it brings about.
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Affiliation(s)
- Mykyta Kabrel
- Institute of Philosophy and Semiotics, University of Tartu, Tartu, Estonia.
| | - Kadi Tulver
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia
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35
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Cone I, Clopath C. Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure. Nat Commun 2024; 15:687. [PMID: 38263408 PMCID: PMC10806076 DOI: 10.1038/s41467-024-44871-6] [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: 05/02/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task's complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called "cognitive map". However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent "splitters") while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.
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Affiliation(s)
- Ian Cone
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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36
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Dyballa L, Rudzite AM, Hoseini MS, Thapa M, Stryker MP, Field GD, Zucker SW. Population encoding of stimulus features along the visual hierarchy. Proc Natl Acad Sci U S A 2024; 121:e2317773121. [PMID: 38227668 PMCID: PMC10823231 DOI: 10.1073/pnas.2317773121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024] Open
Abstract
The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.
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Affiliation(s)
- Luciano Dyballa
- Department of Computer Science, Yale University, New Haven, CT06511
| | | | - Mahmood S. Hoseini
- Department of Physiology, University of California, San Francisco, CA94143
| | - Mishek Thapa
- Department of Neurobiology, Duke University, Durham, NC27708
- Department of Ophthalmology, David Geffen School of Medicine, Stein Eye Institute, University of California, Los Angeles, CA90095
| | - Michael P. Stryker
- Department of Physiology, University of California, San Francisco, CA94143
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA94143
| | - Greg D. Field
- Department of Neurobiology, Duke University, Durham, NC27708
- Department of Ophthalmology, David Geffen School of Medicine, Stein Eye Institute, University of California, Los Angeles, CA90095
| | - Steven W. Zucker
- Department of Computer Science, Yale University, New Haven, CT06511
- Department of Biomedical Engineering, Yale University, New Haven, CT06511
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37
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Liao J, Li J, Qiu Y, Wu X, Liu B, Zhang L, Zhang Y, Peng X, Huang R. Dissociable contributions of the hippocampus and orbitofrontal cortex to representing task space in a social context. Cereb Cortex 2024; 34:bhad447. [PMID: 38011099 PMCID: PMC10793565 DOI: 10.1093/cercor/bhad447] [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: 06/12/2023] [Revised: 10/30/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023] Open
Abstract
The hippocampus (HC) and the orbitofrontal cortex (OFC) jointly encode a map-like representation of a task space to guide behavior. It remains unclear how the OFC and HC interact in encoding this map-like representation, though previous studies indicated that both regions have different functions. We acquired the functional magnetic resonance imaging data under a social navigation task in which participants interacted with characters in a two-dimensional "social space." We calculate the social relationships between the participants and characters and used a drift-diffusion model to capture the inner process of social interaction. Then we used multivoxel pattern analysis to explore the brain-behavior relationship. We found that (i) both the HC and the OFC showed higher activations during the selective trial than the narrative trial; (ii) the neural pattern of the right HC was associated with evidence accumulation during social interaction, and the pattern of the right lateral OFC was associated with the social relationship; (iii) the neural pattern of the HC can decode the participants choices, while the neural pattern of the OFC can decode the task information about trials. The study provided evidence for distinct roles of the HC and the OFC in encoding different information when representing social space.
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Affiliation(s)
- Jiajun Liao
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Jinhui Li
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Yidan Qiu
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Xiaoyan Wu
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Bingyi Liu
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Lu Zhang
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Yuting Zhang
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Xiaoqi Peng
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Ruiwang Huang
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, Guangdong, China
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38
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Shen Z, Kuang S, Zhang Y, Chen J, Wang S, Xu C, Huang Y, Zhang M, Huang S, Wang J, Zhao C, Lin Z, Shi X, Cheng B. Restoring periodontal tissue homoeostasis prevents cognitive decline by reducing the number of Serpina3n high astrocytes in the hippocampus. Innovation (N Y) 2024; 5:100547. [PMID: 38170012 PMCID: PMC10758991 DOI: 10.1016/j.xinn.2023.100547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive decline has been linked to periodontitis through an undetermined pathophysiological mechanism. This study aimed to explore the mechanism underlying periodontitis-related cognitive decline and identify therapeutic strategies for this condition. Using single-nucleus RNA sequencing we found that changes in astrocyte number, gene expression, and cell‒cell communication were associated with cognitive decline in mice with periodontitis. In addition, activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome was observed to decrease the phagocytic capability of macrophages and reprogram macrophages to a more proinflammatory state in the gingiva, thus aggravating periodontitis. To further investigate this finding, lipid-based nanoparticles carrying NLRP3 siRNA (NPsiNLRP3) were used to inhibit overactivation of the NLRP3 inflammasome in gingival macrophages, restoring the oral microbiome and reducing periodontal inflammation. Furthermore, gingival injection of NPsiNLRP3 reduced the number of Serpina3nhigh astrocytes in the hippocampus and prevented cognitive decline. This study provides a functional basis for the mechanism by which the destruction of periodontal tissues can worsen cognitive decline and identifies nanoparticle-mediated restoration of gingival macrophage function as a novel treatment for periodontitis-related cognitive decline.
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Affiliation(s)
- Zongshan Shen
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Shuhong Kuang
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Yong Zhang
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Jiayao Chen
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Shuting Wang
- National Engineering Research Centre for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou 510006, China
| | - Congfei Xu
- School of Biomedical Science and Engineering, South China University of Technology, Guangzhou 510650, China
| | - Yunjia Huang
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Min Zhang
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Shuheng Huang
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Jun Wang
- School of Biomedical Science and Engineering, South China University of Technology, Guangzhou 510650, China
| | - ChuanJiang Zhao
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhengmei Lin
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
| | - Xuetao Shi
- National Engineering Research Centre for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou 510006, China
| | - Bin Cheng
- Hospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510060, China
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39
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Sylte OC, Muysers H, Chen HL, Bartos M, Sauer JF. Neuronal tuning to threat exposure remains stable in the mouse prefrontal cortex over multiple days. PLoS Biol 2024; 22:e3002475. [PMID: 38206890 PMCID: PMC10783789 DOI: 10.1371/journal.pbio.3002475] [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: 06/28/2023] [Accepted: 12/19/2023] [Indexed: 01/13/2024] Open
Abstract
Intense threat elicits action in the form of active and passive coping. The medial prefrontal cortex (mPFC) executes top-level control over the selection of threat coping strategies, but the dynamics of mPFC activity upon continuing threat encounters remain unexplored. Here, we used 1-photon calcium imaging in mice to probe the activity of prefrontal pyramidal cells during repeated exposure to intense threat in a tail suspension (TS) paradigm. A subset of prefrontal neurons displayed selective activation during TS, which was stably maintained over days. During threat, neurons showed specific tuning to active or passive coping. These responses were unrelated to general motion tuning and persisted over days. Moreover, the neural manifold traversed by low-dimensional population activity remained stable over subsequent days of TS exposure and was preserved across individuals. These data thus reveal a specific, temporally, and interindividually conserved repertoire of prefrontal tuning to behavioral responses under threat.
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Affiliation(s)
- Ole Christian Sylte
- University of Freiburg, Medical Faculty, Institute of Physiology I, Freiburg, Germany
- University of Freiburg, Faculty of Biology, Freiburg, Germany
| | - Hannah Muysers
- University of Freiburg, Medical Faculty, Institute of Physiology I, Freiburg, Germany
- University of Freiburg, Faculty of Biology, Freiburg, Germany
| | - Hung-Ling Chen
- University of Freiburg, Medical Faculty, Institute of Physiology I, Freiburg, Germany
| | - Marlene Bartos
- University of Freiburg, Medical Faculty, Institute of Physiology I, Freiburg, Germany
| | - Jonas-Frederic Sauer
- University of Freiburg, Medical Faculty, Institute of Physiology I, Freiburg, Germany
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40
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Elmoznino E, Bonner MF. High-performing neural network models of visual cortex benefit from high latent dimensionality. PLoS Comput Biol 2024; 20:e1011792. [PMID: 38198504 PMCID: PMC10805290 DOI: 10.1371/journal.pcbi.1011792] [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: 07/18/2023] [Revised: 01/23/2024] [Accepted: 12/30/2023] [Indexed: 01/12/2024] Open
Abstract
Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. A popular view holds that optimal DNNs compress their representations onto low-dimensional subspaces to achieve invariance and robustness, which suggests that better models of visual cortex should have lower dimensional geometries. Surprisingly, we found a strong trend in the opposite direction-neural networks with high-dimensional image subspaces tended to have better generalization performance when predicting cortical responses to held-out stimuli in both monkey electrophysiology and human fMRI data. Moreover, we found that high dimensionality was associated with better performance when learning new categories of stimuli, suggesting that higher dimensional representations are better suited to generalize beyond their training domains. These findings suggest a general principle whereby high-dimensional geometry confers computational benefits to DNN models of visual cortex.
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Affiliation(s)
- Eric Elmoznino
- Department of Cognitive Science, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael F. Bonner
- Department of Cognitive Science, Johns Hopkins University, Baltimore, Maryland, United States of America
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41
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Bi Z. Cognition of Time and Thinking Beyond. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1455:171-195. [PMID: 38918352 DOI: 10.1007/978-3-031-60183-5_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.
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Affiliation(s)
- Zedong Bi
- Lingang Laboratory, Shanghai, China.
- Institute for Future, Qingdao University, Qingdao, China.
- School of Automation, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, China.
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42
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Brown LS, Cho JR, Bolkan SS, Nieh EH, Schottdorf M, Tank DW, Brody CD, Witten IB, Goldman MS. Neural circuit models for evidence accumulation through choice-selective sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555612. [PMID: 38234715 PMCID: PMC10793437 DOI: 10.1101/2023.09.01.555612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.
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43
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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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44
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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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45
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Courellis HS, Mixha J, Cardenas AR, Kimmel D, Reed CM, Valiante TA, Salzman CD, Mamelak AN, Fusi S, Rutishauser U. Abstract representations emerge in human hippocampal neurons during inference behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566490. [PMID: 37986878 PMCID: PMC10659400 DOI: 10.1101/2023.11.10.566490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization 1 . However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior 2,3 . Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.
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46
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Luo TZ, Kim TD, Gupta D, Bondy AG, Kopec CD, Elliot VA, DePasquale B, Brody CD. Transitions in dynamical regime and neural mode underlie perceptual decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562427. [PMID: 37904994 PMCID: PMC10614809 DOI: 10.1101/2023.10.15.562427] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We show that contrary to prevailing hypotheses, attractors play a role only after a transition from a regime in the dynamics that is strongly driven by inputs to one dominated by the intrinsic dynamics. The initial regime mediates evidence accumulation, and the subsequent intrinsic-dominant regime subserves decision commitment. This regime transition is coupled to a rapid reorganization in the representation of the decision process in the neural population (a change in the "neural mode" along which the process develops). A simplified model approximating the coupled transition in the dynamics and neural mode allows inferring, from each trial's neural activity, the internal decision commitment time in that trial, and captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest pairing deep learning and parsimonious models as a promising approach for understanding complex data.
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47
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Rizzoglio F, Altan E, Ma X, Bodkin KL, Dekleva BM, Solla SA, Kennedy A, Miller LE. From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis. J Neural Eng 2023; 20:056040. [PMID: 37844567 PMCID: PMC10618714 DOI: 10.1088/1741-2552/ad038e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.
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Affiliation(s)
- Fabio Rizzoglio
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Ege Altan
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
| | - Xuan Ma
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Kevin L Bodkin
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Brian M Dekleva
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sara A Solla
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, United States of America
| | - Ann Kennedy
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Lee E Miller
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
- Shirley Ryan AbilityLab, Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
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48
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Tsuda B, Richmond BJ, Sejnowski TJ. Exploring strategy differences between humans and monkeys with recurrent neural networks. PLoS Comput Biol 2023; 19:e1011618. [PMID: 37983250 PMCID: PMC10695363 DOI: 10.1371/journal.pcbi.1011618] [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: 02/19/2023] [Revised: 12/04/2023] [Accepted: 10/19/2023] [Indexed: 11/22/2023] Open
Abstract
Animal models are used to understand principles of human biology. Within cognitive neuroscience, non-human primates are considered the premier model for studying decision-making behaviors in which direct manipulation experiments are still possible. Some prominent studies have brought to light major discrepancies between monkey and human cognition, highlighting problems with unverified extrapolation from monkey to human. Here, we use a parallel model system-artificial neural networks (ANNs)-to investigate a well-established discrepancy identified between monkeys and humans with a working memory task, in which monkeys appear to use a recency-based strategy while humans use a target-selective strategy. We find that ANNs trained on the same task exhibit a progression of behavior from random behavior (untrained) to recency-like behavior (partially trained) and finally to selective behavior (further trained), suggesting monkeys and humans may occupy different points in the same overall learning progression. Surprisingly, what appears to be recency-like behavior in the ANN, is in fact an emergent non-recency-based property of the organization of the neural network's state space during its development through training. We find that explicit encouragement of recency behavior during training has a dual effect, not only causing an accentuated recency-like behavior, but also speeding up the learning process altogether, resulting in an efficient shaping mechanism to achieve the optimal strategy. Our results suggest a new explanation for the discrepency observed between monkeys and humans and reveal that what can appear to be a recency-based strategy in some cases may not be recency at all.
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Affiliation(s)
- Ben Tsuda
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America
- Neurosciences Graduate Program, University of California San Diego, La Jolla, California, United States of America
- Medical Scientist Training Program, University of California San Diego, La Jolla, California, United States of America
| | - Barry J. Richmond
- Section on Neural Coding and Computation, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Terrence J. Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America
- Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America
- Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America
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49
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Srinivasan A, Srinivasan A, Riceberg JS, Goodman MR, Guise KG, Shapiro ML. Hippocampal and medial prefrontal ensemble spiking represents episodes and rules in similar task spaces. Cell Rep 2023; 42:113296. [PMID: 37858467 PMCID: PMC10842596 DOI: 10.1016/j.celrep.2023.113296] [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: 07/25/2022] [Revised: 08/22/2023] [Accepted: 10/03/2023] [Indexed: 10/21/2023] Open
Abstract
Episodic memory requires the hippocampus and prefrontal cortex to guide decisions by representing events in spatial, temporal, and personal contexts. Both brain regions have been described by cognitive theories that represent events in context as locations in maps or memory spaces. We query whether ensemble spiking in these regions described spatial structures as rats performed memory tasks. From each ensemble, we construct a state-space with each point defined by the coordinated spiking of single and pairs of units in 125-ms bins and investigate how state-space locations discriminate task features. Trajectories through state-spaces correspond with behavioral episodes framed by spatial, temporal, and internal contexts. Both hippocampal and prefrontal ensembles distinguish maze locations, task intervals, and goals by distances between state-space locations, consistent with cognitive mapping and relational memory space theories of episodic memory. Prefrontal modulation of hippocampal activity may guide choices by directing memory representations toward appropriate state-space goal locations.
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Affiliation(s)
- Aditya Srinivasan
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA.
| | - Arvind Srinivasan
- College of Health Sciences, California Northstate University, Rancho Cordova, CA 95670, USA
| | - Justin S Riceberg
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, USA
| | - Michael R Goodman
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA
| | - Kevin G Guise
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, USA
| | - Matthew L Shapiro
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA.
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50
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Levy ERJ, Carrillo-Segura S, Park EH, Redman WT, Hurtado JR, Chung S, Fenton AA. A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields. Cell Rep 2023; 42:113142. [PMID: 37742193 PMCID: PMC10842170 DOI: 10.1016/j.celrep.2023.113142] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/14/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.
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Affiliation(s)
| | - Simón Carrillo-Segura
- Center for Neural Science, New York University, New York, NY 10003, USA; Graduate Program in Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
| | - Eun Hye Park
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - William Thomas Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY 10003, USA; Flatiron Institute Center for Computational Neuroscience, New York, NY 10010, USA
| | - André Antonio Fenton
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute at the NYU Langone Medical Center, New York, NY 10016, USA.
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