1
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Kanemura I, Kitano K. Emergence of input selective recurrent dynamics via information transfer maximization. Sci Rep 2024; 14:13631. [PMID: 38871759 DOI: 10.1038/s41598-024-64417-6] [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: 03/11/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.
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Affiliation(s)
- Itsuki Kanemura
- Graduate School of Information Science and Engineering, Ritsumeikan University, 2-150, Iwakuracho, Ibaraki, Osaka, 5670871, Japan.
| | - Katsunori Kitano
- Department of Information Science and Engineering, Ritsumeikan University, 2-150, Iwakuracho, Ibaraki, Osaka, 5670871, Japan
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2
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Liu Y, Wang XJ. Flexible gating between subspaces in a neural network model of internally guided task switching. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.15.553375. [PMID: 37645801 PMCID: PMC10462002 DOI: 10.1101/2023.08.15.553375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.
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3
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Mahowald K, Ivanova AA, Blank IA, Kanwisher N, Tenenbaum JB, Fedorenko E. Dissociating language and thought in large language models. Trends Cogn Sci 2024; 28:517-540. [PMID: 38508911 DOI: 10.1016/j.tics.2024.01.011] [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: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 03/22/2024]
Abstract
Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.
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4
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Chai M, Holroyd CB, Brass M, Braem S. Dynamic changes in task preparation in a multi-task environment: The task transformation paradigm. Cognition 2024; 247:105784. [PMID: 38599142 DOI: 10.1016/j.cognition.2024.105784] [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: 09/29/2023] [Revised: 02/13/2024] [Accepted: 03/25/2024] [Indexed: 04/12/2024]
Abstract
A key element of human flexible behavior concerns the ability to continuously predict and prepare for sudden changes in tasks or actions. Here, we tested whether people can dynamically modulate task preparation processes and decision-making strategies when the identity of a to-be-performed task becomes uncertain. To this end, we developed a new paradigm where participants need to prepare for one of nine tasks on each trial. Crucially, in some blocks, the task being prepared could suddenly shift to a different task after a longer cue-target interval, by changing either the stimulus category or categorization rule that defined the initial task. We found that participants were able to dynamically modulate task preparation in the face of this task uncertainty. A second experiment shows that these changes in behavior were not simply a function of decreasing task expectancy, but rather of increasing switch expectancy. Finally, in the third and fourth experiment, we demonstrate that these dynamic modulations can be applied in a compositional manner, depending on whether either only the stimulus category or categorization rule would be expected to change.
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Affiliation(s)
- Mengqiao Chai
- Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium.
| | - Clay B Holroyd
- Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium.
| | - Marcel Brass
- Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium; Berlin School of Mind and Brain, Department of Psychology, Humboldt-Universität zu Berlin, Luisenstraße 56, Haus 1, 10117 Berlin, Germany.
| | - Senne Braem
- Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium.
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5
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Marrero K, Aruljothi K, Delgadillo C, Kabbara S, Swatch L, Zagha E. Goal-Directed Learning is Multidimensional and Accompanied by Diverse and Widespread Changes in Neocortical Signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.13.528412. [PMID: 36824924 PMCID: PMC9948952 DOI: 10.1101/2023.02.13.528412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of licking-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.
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Affiliation(s)
- Krista Marrero
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Krithiga Aruljothi
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Christian Delgadillo
- Division of Biomedical Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Sarah Kabbara
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Lovleen Swatch
- College of Natural & Agricultural Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Edward Zagha
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
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6
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Proca AM, Rosas FE, Luppi AI, Bor D, Crosby M, Mediano PAM. Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks. PLoS Comput Biol 2024; 20:e1012178. [PMID: 38829900 PMCID: PMC11175422 DOI: 10.1371/journal.pcbi.1012178] [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: 11/07/2023] [Revised: 06/13/2024] [Accepted: 05/18/2024] [Indexed: 06/05/2024] Open
Abstract
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.
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Affiliation(s)
- Alexandra M. Proca
- Department of Computing, Imperial College London, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Sussex Centre for Consciousness Science and Sussex AI, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research and Centre for Complexity Science, Department of Brain Sciences, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Andrea I. Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, Queen Mary University of London, London, United Kingdom
| | - Matthew Crosby
- Department of Computing, Imperial College London, London, United Kingdom
| | - Pedro A. M. Mediano
- Department of Computing, Imperial College London, London, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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7
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Forbes CE. On the neural networks of self and other bias and their role in emergent social interactions. Cortex 2024; 177:113-129. [PMID: 38848651 DOI: 10.1016/j.cortex.2024.05.002] [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: 06/01/2023] [Revised: 02/09/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.
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Affiliation(s)
- Chad E Forbes
- Social Neuroscience Laboratory, Department of Psychology, Florida Atlantic University, Boca Raton, FL, USA; Florida Atlantic University Stiles-Nicholson Brain Institute, USA.
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8
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Mastrovito D, Liu YH, Kusmierz L, Shea-Brown E, Koch C, Mihalas S. Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594236. [PMID: 38798582 PMCID: PMC11118502 DOI: 10.1101/2024.05.15.594236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recurrent neural networks exhibit chaotic dynamics when the variance in their connection strengths exceed a critical value. Recent work indicates connection variance also modulates learning strategies; networks learn "rich" representations when initialized with low coupling and "lazier" solutions with larger variance. Using Watts-Strogatz networks of varying sparsity, structure, and hidden weight variance, we find that the critical coupling strength dividing chaotic from ordered dynamics also differentiates rich and lazy learning strategies. Training moves both stable and chaotic networks closer to the edge of chaos, with networks learning richer representations before the transition to chaos. In contrast, biologically realistic connectivity structures foster stability over a wide range of variances. The transition to chaos is also reflected in a measure that clinically discriminates levels of consciousness, the perturbational complexity index (PCIst). Networks with high values of PCIst exhibit stable dynamics and rich learning, suggesting a consciousness prior may promote rich learning. The results suggest a clear relationship between critical dynamics, learning regimes and complexity-based measures of consciousness.
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9
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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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10
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Levi A, Aviv N, Stark E. Learning to learn: Single session acquisition of new rules by freely moving mice. PNAS NEXUS 2024; 3:pgae203. [PMID: 38818240 PMCID: PMC11138122 DOI: 10.1093/pnasnexus/pgae203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024]
Abstract
Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.
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Affiliation(s)
- Amir Levi
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Noam Aviv
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol Department of Neurobiology, Haifa University, Haifa 3103301, Israel
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11
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Gong L, Pasqualetti F, Papouin T, Ching S. Astrocytes as a mechanism for contextually-guided network dynamics and function. PLoS Comput Biol 2024; 20:e1012186. [PMID: 38820533 PMCID: PMC11168681 DOI: 10.1371/journal.pcbi.1012186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/12/2024] [Accepted: 05/21/2024] [Indexed: 06/02/2024] Open
Abstract
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.
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Affiliation(s)
- Lulu Gong
- Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri, United States of America
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, California, United States of America
| | - Thomas Papouin
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri, United States of America
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12
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Terada Y, Toyoizumi T. Chaotic neural dynamics facilitate probabilistic computations through sampling. Proc Natl Acad Sci U S A 2024; 121:e2312992121. [PMID: 38648479 PMCID: PMC11067032 DOI: 10.1073/pnas.2312992121] [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: 07/28/2023] [Accepted: 02/13/2024] [Indexed: 04/25/2024] Open
Abstract
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
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Affiliation(s)
- Yu Terada
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
- The Institute for Physics of Intelligence, The University of Tokyo, Tokyo113-0033, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo113-8656, Japan
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13
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Rush ER, Heckman C, Jayaram K, Humbert JS. Neural dynamics of robust legged robots. Front Robot AI 2024; 11:1324404. [PMID: 38699630 PMCID: PMC11063321 DOI: 10.3389/frobt.2024.1324404] [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: 10/19/2023] [Accepted: 03/26/2024] [Indexed: 05/05/2024] Open
Abstract
Legged robot control has improved in recent years with the rise of deep reinforcement learning, however, much of the underlying neural mechanisms remain difficult to interpret. Our aim is to leverage bio-inspired methods from computational neuroscience to better understand the neural activity of robust robot locomotion controllers. Similar to past work, we observe that terrain-based curriculum learning improves agent stability. We study the biomechanical responses and neural activity within our neural network controller by simultaneously pairing physical disturbances with targeted neural ablations. We identify an agile hip reflex that enables the robot to regain its balance and recover from lateral perturbations. Model gradients are employed to quantify the relative degree that various sensory feedback channels drive this reflexive behavior. We also find recurrent dynamics are implicated in robust behavior, and utilize sampling-based ablation methods to identify these key neurons. Our framework combines model-based and sampling-based methods for drawing causal relationships between neural network activity and robust embodied robot behavior.
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Affiliation(s)
- Eugene R. Rush
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States
| | - Christoffer Heckman
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
| | - Kaushik Jayaram
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States
| | - J. Sean Humbert
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States
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14
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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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15
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Lyu B, Marslen-Wilson WD, Fang Y, Tyler LK. Finding structure during incremental speech comprehension. eLife 2024; 12:RP89311. [PMID: 38577982 PMCID: PMC10997333 DOI: 10.7554/elife.89311] [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: 04/06/2024] Open
Abstract
A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker's intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.
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Affiliation(s)
| | - William D Marslen-Wilson
- Centre for Speech, Language and the Brain, Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Yuxing Fang
- Centre for Speech, Language and the Brain, Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Lorraine K Tyler
- Centre for Speech, Language and the Brain, Department of Psychology, University of CambridgeCambridgeUnited Kingdom
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16
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Churchland MM, Shenoy KV. Preparatory activity and the expansive null-space. Nat Rev Neurosci 2024; 25:213-236. [PMID: 38443626 DOI: 10.1038/s41583-024-00796-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
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Affiliation(s)
- Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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17
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Tafazoli S, Bouchacourt FM, Ardalan A, Markov NT, Uchimura M, Mattar MG, Daw ND, Buschman TJ. Building compositional tasks with shared neural subspaces. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578263. [PMID: 38352540 PMCID: PMC10862921 DOI: 10.1101/2024.01.31.578263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks1. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations2 and computational components3 across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks4. Yet, whether such compositionality is found in the brain is unknown. Here, we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task compositionally combining these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. Neural recordings found task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Subspaces were flexibly engaged as monkeys discovered the task in effect; their internal belief about the current task predicted the strength of representations in task-relevant subspaces. In sum, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations across tasks.
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Affiliation(s)
- Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Adel Ardalan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nikola T. Markov
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Motoaki Uchimura
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Nathaniel D. Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
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18
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Xue C, Markman SK, Chen R, Kramer LE, Cohen MR. Task interference as a neuronal basis for the cost of cognitive flexibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583375. [PMID: 38496626 PMCID: PMC10942291 DOI: 10.1101/2024.03.04.583375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Humans and animals have an impressive ability to juggle multiple tasks in a constantly changing environment. This flexibility, however, leads to decreased performance under uncertain task conditions. Here, we combined monkey electrophysiology, human psychophysics, and artificial neural network modeling to investigate the neuronal mechanisms of this performance cost. We developed a behavioural paradigm to measure and influence participants' decision-making and perception in two distinct perceptual tasks. Our data revealed that both humans and monkeys, unlike an artificial neural network trained for the same tasks, make less accurate perceptual decisions when the task is uncertain. We generated a mechanistic hypothesis by comparing this neural network trained to produce correct choices with another network trained to replicate the participants' choices. We hypothesized, and confirmed with further behavioural, physiological, and causal experiments, that the cost of task flexibility comes from what we term task interference. Under uncertain conditions, interference between different tasks causes errors because it results in a stronger representation of irrelevant task features and entangled neuronal representations of different features. Our results suggest a tantalizing, general hypothesis: that cognitive capacity limitations, both in health and disease, stem from interference between neural representations of different stimuli, tasks, or memories.
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Affiliation(s)
- Cheng Xue
- Department of Neurobiology, University of Chicago, IL, USA
| | - Sol K Markman
- Department of Neurobiology, University of Chicago, IL, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Ruoyi Chen
- Department of Biological Sciences, Carnegie Mellon University, PA, USA
| | - Lily E Kramer
- Department of Neurobiology, University of Chicago, IL, USA
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19
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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.16.537079. [PMID: 37163104 PMCID: PMC10168209 DOI: 10.1101/2023.04.16.537079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
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20
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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. PLoS Comput Biol 2024; 20:e1011943. [PMID: 38547053 PMCID: PMC10977720 DOI: 10.1371/journal.pcbi.1011943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 02/24/2024] [Indexed: 04/02/2024] Open
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida, United States of America
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21
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Ichikawa K, Kaneko K. Bayesian inference is facilitated by modular neural networks with different time scales. PLoS Comput Biol 2024; 20:e1011897. [PMID: 38478575 PMCID: PMC10962854 DOI: 10.1371/journal.pcbi.1011897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/25/2024] [Accepted: 02/06/2024] [Indexed: 03/26/2024] Open
Abstract
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. Our findings reveal that networks with modular structures, composed of fast and slow modules, are adept at representing this prior distribution, enabling more accurate Bayesian inferences. Specifically, the modular network that consists of a main module connected with input and output layers and a sub-module with slower neural activity connected only with the main module outperformed networks with uniform time scales. Prior information was represented specifically by the slow sub-module, which could integrate observed signals over an appropriate period and represent input means and variances. Accordingly, the neural network could effectively predict the time-varying inputs. Furthermore, by training the time scales of neurons starting from networks with uniform time scales and without modular structure, the above slow-fast modular network structure and the division of roles in which prior knowledge is selectively represented in the slow sub-modules spontaneously emerged. These results explain how the prior distribution for Bayesian inference is represented in the brain, provide insight into the relevance of modular structure with time scale hierarchy to information processing, and elucidate the significance of brain areas with slower time scales.
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Affiliation(s)
- Kohei Ichikawa
- Department of Basic Science, Graduate School of Arts and Sciences, University of Tokyo, Meguro-ku, Tokyo, Japan
| | - Kunihiko Kaneko
- Research Center for Complex Systems Biology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej, Copenhagen, Denmark
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22
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Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578988. [PMID: 38370650 PMCID: PMC10871230 DOI: 10.1101/2024.02.05.578988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
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Affiliation(s)
- Andrew J Zimnik
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - K Cora Ames
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Xinyue An
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA
| | - Laura Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, CA, USA
| | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Vladislav Susoy
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Joshua I Glaser
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
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23
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Chen Z, Han Y, Ma Z, Wang X, Xu S, Tang Y, Vyssotski AL, Si B, Zhan Y. A prefrontal-thalamic circuit encodes social information for social recognition. Nat Commun 2024; 15:1036. [PMID: 38310109 PMCID: PMC10838311 DOI: 10.1038/s41467-024-45376-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Social recognition encompasses encoding social information and distinguishing unfamiliar from familiar individuals to form social relationships. Although the medial prefrontal cortex (mPFC) is known to play a role in social behavior, how identity information is processed and by which route it is communicated in the brain remains unclear. Here we report that a ventral midline thalamic area, nucleus reuniens (Re) that has reciprocal connections with the mPFC, is critical for social recognition in male mice. In vivo single-unit recordings and decoding analysis reveal that neural populations in both mPFC and Re represent different social stimuli, however, mPFC coding capacity is stronger. We demonstrate that chemogenetic inhibitions of Re impair the mPFC-Re neural synchronization and the mPFC social coding. Projection pathway-specific inhibitions by optogenetics reveal that the reciprocal connectivity between the mPFC and the Re is necessary for social recognition. These results reveal an mPFC-thalamic circuit for social information processing.
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Affiliation(s)
- Zihao Chen
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yechao Han
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zheng Ma
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xinnian Wang
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Surui Xu
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yong Tang
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Alexei L Vyssotski
- Institute of Neuroinformatics, University of Zurich and Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Bailu Si
- School of Systems Science, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Yang Zhan
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- CAS Key Laboratory of Brain Connectome and Manipulation, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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24
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Stern M, Liu AJ, Balasubramanian V. Physical effects of learning. Phys Rev E 2024; 109:024311. [PMID: 38491658 DOI: 10.1103/physreve.109.024311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Abstract
Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning in linear physical networks with weak input signals leaves architectural imprints on the Hessian of a physical system. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs decreases, (b) the response of physical degrees of freedom to random perturbations (or system "susceptibility") increases, and (c) the low-eigenvalue eigenvectors of the Hessian align with the task. Overall, these effects embody the typical scenario for learning processes in physical systems in the weak input regime, suggesting ways of discovering whether a physical network may have been trained.
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Affiliation(s)
- Menachem Stern
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Andrea J Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York 10010, USA
| | - Vijay Balasubramanian
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA
- Theoretische Natuurkunde, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
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25
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Ding X, Froudist-Walsh S, Jaramillo J, Jiang J, Wang XJ. Cell type-specific connectome predicts distributed working memory activity in the mouse brain. eLife 2024; 13:e85442. [PMID: 38174734 PMCID: PMC10807864 DOI: 10.7554/elife.85442] [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: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.
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Affiliation(s)
- Xingyu Ding
- Center for Neural Science, New York UniversityNew YorkUnited States
| | - Sean Froudist-Walsh
- Center for Neural Science, New York UniversityNew YorkUnited States
- Bristol Computational Neuroscience Unit, School of Engineering Mathematics and Technology, University of BristolBristolUnited Kingdom
| | - Jorge Jaramillo
- Center for Neural Science, New York UniversityNew YorkUnited States
- Campus Institute for Dynamics of Biological Networks, University of GöttingenGöttingenGermany
| | - Junjie Jiang
- Center for Neural Science, New York UniversityNew YorkUnited States
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,Institute of Health and Rehabilitation Science,School of Life Science and Technology, Research Center for Brain-inspired Intelligence, Xi’an Jiaotong UniversityXi'anChina
| | - Xiao-Jing Wang
- Center for Neural Science, New York UniversityNew YorkUnited States
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26
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Oby ER, Degenhart AD, Grigsby EM, Motiwala A, McClain NT, Marino PJ, Yu BM, Batista AP. Dynamical constraints on neural population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.573543. [PMID: 38260549 PMCID: PMC10802336 DOI: 10.1101/2024.01.03.573543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.
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27
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Idei H, Yamashita Y. Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models. Neural Netw 2024; 169:57-74. [PMID: 37857173 DOI: 10.1016/j.neunet.2023.10.005] [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: 03/27/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.
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Affiliation(s)
- Hayato Idei
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan.
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28
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Rao RPN, Gklezakos DC, Sathish V. Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning. Neural Comput 2023; 36:1-32. [PMID: 38052084 DOI: 10.1162/neco_a_01627] [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: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 12/07/2023]
Abstract
There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active predictive coding (APC) as a unifying model for perception, action, and cognition. The APC model addresses important open problems in cognitive science and AI, including (1) how we learn compositional representations (e.g., part-whole hierarchies for equivariant vision) and (2) how we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex state dynamics and abstract actions from simpler dynamics and primitive actions. By using hypernetworks, self-supervised learning, and reinforcement learning, APC learns hierarchical world models by combining task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.
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Affiliation(s)
- Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering and Center for Neurotechnology, University of Washington, Seattle, WA 98195, U.S.A.
| | - Dimitrios C Gklezakos
- Paul G. Allen School of Computer Science and Engineering and Center for Neurotechnology, University of Washington, Seattle, WA 98195, U.S.A.
| | - Vishwas Sathish
- Paul G. Allen School of Computer Science and Engineering and Center for Neurotechnology, University of Washington, Seattle, WA 98195, U.S.A.
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Stern M, Istrate N, Mazzucato L. A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies. eLife 2023; 12:e86552. [PMID: 38084779 PMCID: PMC10810607 DOI: 10.7554/elife.86552] [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: 01/31/2023] [Accepted: 12/07/2023] [Indexed: 01/26/2024] Open
Abstract
The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here, we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking networks with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in the awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies.
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Affiliation(s)
- Merav Stern
- Institute of Neuroscience, University of OregonEugeneUnited States
- Faculty of Medicine, The Hebrew University of JerusalemJerusalemIsrael
| | - Nicolae Istrate
- Institute of Neuroscience, University of OregonEugeneUnited States
- Departments of Physics, University of OregonEugeneUnited States
| | - Luca Mazzucato
- Institute of Neuroscience, University of OregonEugeneUnited States
- Departments of Physics, University of OregonEugeneUnited States
- Mathematics and Biology, University of OregonEugeneUnited States
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30
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Thrower L, Dang W, Jaffe RG, Sun JD, Constantinidis C. Decoding working memory information from neurons with and without persistent activity in the primate prefrontal cortex. J Neurophysiol 2023; 130:1392-1402. [PMID: 37910532 PMCID: PMC11068397 DOI: 10.1152/jn.00290.2023] [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: 07/31/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023] Open
Abstract
Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have challenged this idea. Theories that depend on the dynamic representation of information posit that stimulus information may be maintained by the activity pattern of neurons whose firing rate is not significantly elevated above their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Averaged across all neurons and stimuli, the firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory; however, this grand average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of stimulus information. These results reveal that prefrontal neurons that generate persistent activity maintain information more reliably during working memory.NEW & NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. Although the two models have been debated on theoretical terms, direct comparison of empirical results has been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
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Affiliation(s)
- Lilianna Thrower
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Wenhao Dang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Rye G Jaffe
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Jasmine D Sun
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
- Neuroscience Program, Vanderbilt University, Nashville, Tennessee, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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31
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Gurnani H, Cayco Gajic NA. Signatures of task learning in neural representations. Curr Opin Neurobiol 2023; 83:102759. [PMID: 37708653 DOI: 10.1016/j.conb.2023.102759] [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: 04/14/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Abstract
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.
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Affiliation(s)
- Harsha Gurnani
- Department of Biology, University of Washington, Seattle, WA, USA. https://twitter.com/HarshaGurnani
| | - N Alex Cayco Gajic
- Laboratoire de Neuroscience Cognitives, Ecole Normale Supérieure, Université PSL, Paris, France.
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32
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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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Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. Proc Natl Acad Sci U S A 2023; 120:e2307991120. [PMID: 37983510 PMCID: PMC10691340 DOI: 10.1073/pnas.2307991120] [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: 05/15/2023] [Accepted: 09/29/2023] [Indexed: 11/22/2023] Open
Abstract
Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.
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Affiliation(s)
- Jake P. Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Kei Watanabe
- Graduate School of Frontier Biosciences, Osaka University, Osaka565-0871, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Communication and Information Technology, Osaka565-0871, Japan
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, BudapestH-1051, Hungary
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34
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Avcu E, Gow D. Exploring Abstract Pattern Representation in The Brain and Non-symbolic Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568877. [PMID: 38076846 PMCID: PMC10705297 DOI: 10.1101/2023.11.27.568877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Human cognitive and linguistic generativity depends on the ability to identify abstract relationships between perceptually dissimilar items. Marcus et al. (1999) found that human infants can rapidly discover and generalize patterns of syllable repetition (reduplication) that depend on the abstract property of identity, but simple recurrent neural networks (SRNs) could not. They interpreted these results as evidence that purely associative neural network models provide an inadequate framework for characterizing the fundamental generativity of human cognition. Here, we present a series of deep long short-term memory (LSTM) models that identify abstract syllable repetition patterns and words based on training with cochleagrams that represent auditory stimuli. We demonstrate that models trained to identify individual syllable trigram words and models trained to identify reduplication patterns discover representations that support classification of abstract repetition patterns. Simulations examined the effects of training categories (words vs. patterns) and pretraining to identify syllables, on the development of hidden node representations that support repetition pattern discrimination. Representational similarity analyses (RSA) comparing patterns of regional brain activity based on MRI-constrained MEG/EEG data to patterns of hidden node activation elicited by the same stimuli showed a significant correlation between brain activity localized in primarily posterior temporal regions and representations discovered by the models. These results suggest that associative mechanisms operating over discoverable representations that capture abstract stimulus properties account for a critical example of human cognitive generativity.
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Affiliation(s)
- Enes Avcu
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA 02170
| | - David Gow
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA 02170
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35
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Kim TD, Luo TZ, Can T, Krishnamurthy K, Pillow JW, Brody CD. Flow-field inference from neural data using deep recurrent networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567136. [PMID: 38014290 PMCID: PMC10680687 DOI: 10.1101/2023.11.14.567136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity. Using population spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR outperforms existing methods in capturing the heterogeneous responses of individual neurons. We further show that FINDR can discover interpretable low-dimensional dynamics when it is trained to disentangle task-relevant and irrelevant components of the neural population activity. Importantly, the low-dimensional nature of the learned dynamics allows for explicit visualization of flow fields and attractor structures. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.
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Affiliation(s)
| | - Thomas Zhihao Luo
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Tankut Can
- School of Natural Sciences, Institute for Advanced Study, Princeton, NJ
| | - Kamesh Krishnamurthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ
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36
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Jarne C, Laje R. Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks. J Comput Neurosci 2023; 51:407-431. [PMID: 37561278 DOI: 10.1007/s10827-023-00857-9] [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: 10/25/2022] [Revised: 05/26/2023] [Accepted: 06/27/2023] [Indexed: 08/11/2023]
Abstract
Recurrent Neural Networks (RNNs) are frequently used to model aspects of brain function and structure. In this work, we trained small fully-connected RNNs to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and also how the performance gracefully degrades as either network size is decreased, interval duration is increased, or connectivity damage is induced. For the considered tasks, we explored how robust the network obtained after training can be according to task parameterization. In the process, we developed a framework that can be useful to parameterize other tasks of interest in computational neuroscience. Our results are useful to quantify different aspects of the models, which are normally used as black boxes and need to be understood in order to model the biological response of cerebral cortex areas.
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Affiliation(s)
- Cecilia Jarne
- Universidad Nacional de Quilmes, Departamento de Ciencia y Tecnología, Bernal, Buenos Aires, Argentina.
- CONICET, Buenos Aires, Argentina.
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Rodrigo Laje
- Universidad Nacional de Quilmes, Departamento de Ciencia y Tecnología, Bernal, Buenos Aires, Argentina
- CONICET, Buenos Aires, Argentina
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37
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Finn ES, Poldrack RA, Shine JM. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 2023; 623:263-273. [PMID: 37938706 DOI: 10.1038/s41586-023-06670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023]
Abstract
Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA.
| | | | - James M Shine
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
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38
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Durstewitz D, Koppe G, Thurm MI. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat Rev Neurosci 2023; 24:693-710. [PMID: 37794121 DOI: 10.1038/s41583-023-00740-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.
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Affiliation(s)
- Daniel Durstewitz
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
| | - Georgia Koppe
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Dept. of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Max Ingo Thurm
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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39
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Fisher A, Rao RPN. Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars. PNAS NEXUS 2023; 2:pgad337. [PMID: 37954157 PMCID: PMC10637337 DOI: 10.1093/pnasnexus/pgad337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023]
Abstract
Human vision, thought, and planning involve parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using neural networks, but a generative model formulation has been lacking. Generative models that leverage compositionality, recursion, and part-whole hierarchies are thought to underlie human concept learning and the ability to construct and represent flexible mental concepts. We introduce Recursive Neural Programs (RNPs), a neural generative model that addresses the part-whole hierarchy learning problem by modeling images as hierarchical trees of probabilistic sensory-motor programs. These programs recursively reuse learned sensory-motor primitives to model an image within different spatial reference frames, enabling hierarchical composition of objects from parts and implementing a grammar for images. We show that RNPs can learn part-whole hierarchies for a variety of image datasets, allowing rich compositionality and intuitive parts-based explanations of objects. Our model also suggests a cognitive framework for understanding how human brains can potentially learn and represent concepts in terms of recursively defined primitives and their relations with each other.
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Affiliation(s)
- Ares Fisher
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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40
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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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41
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Shah NP, Avansino D, Kamdar F, Nicolas C, Kapitonava A, Vargas-Irwin C, Hochberg L, Pandarinath C, Shenoy K, Willett FR, Henderson J. Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.11.561982. [PMID: 37873182 PMCID: PMC10592742 DOI: 10.1101/2023.10.11.561982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.
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Affiliation(s)
| | - Donald Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | - Claire Nicolas
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Kapitonava
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Vargas-Irwin
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Leigh Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Krishna Shenoy
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Jaimie Henderson
- Department of Neurosurgery, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
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Soo WWM, Goudar V, Wang XJ. Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561588. [PMID: 37873445 PMCID: PMC10592728 DOI: 10.1101/2023.10.10.561588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience. Yet, a major technical hindrance remains. Cognitive processes such as working memory and decision making involve neural population dynamics over a long period of time within a behavioral trial and across trials. It is difficult to train RNNs to accomplish tasks where neural representations and dynamics have long temporal dependencies without gating mechanisms such as LSTMs or GRUs which currently lack experimental support and prohibit direct comparison between RNNs and biological neural circuits. We tackled this problem based on the idea of specialized skip-connections through time to support the emergence of task-relevant dynamics, and subsequently reinstitute biological plausibility by reverting to the original architecture. We show that this approach enables RNNs to successfully learn cognitive tasks that prove impractical if not impossible to learn using conventional methods. Over numerous tasks considered here, we achieve less training steps and shorter wall-clock times, particularly in tasks that require learning long-term dependencies via temporal integration over long timescales or maintaining a memory of past events in hidden-states. Our methods expand the range of experimental tasks that biologically plausible RNN models can learn, thereby supporting the development of theory for the emergent neural mechanisms of computations involving long-term dependencies.
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Khona M, Chandra S, Ma JJ, Fiete IR. Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs. Neural Comput 2023; 35:1850-1869. [PMID: 37725708 DOI: 10.1162/neco_a_01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/20/2023] [Indexed: 09/21/2023]
Abstract
Recurrent neural networks (RNNs) are often used to model circuits in the brain and can solve a variety of difficult computational problems requiring memory, error correction, or selection (Hopfield, 1982; Maass et al., 2002; Maass, 2011). However, fully connected RNNs contrast structurally with their biological counterparts, which are extremely sparse (about 0.1%). Motivated by the neocortex, where neural connectivity is constrained by physical distance along cortical sheets and other synaptic wiring costs, we introduce locality masked RNNs (LM-RNNs) that use task-agnostic predetermined graphs with sparsity as low as 4%. We study LM-RNNs in a multitask learning setting relevant to cognitive systems neuroscience with a commonly used set of tasks, 20-Cog-tasks (Yang et al., 2019). We show through reductio ad absurdum that 20-Cog-tasks can be solved by a small pool of separated autapses that we can mechanistically analyze and understand. Thus, these tasks fall short of the goal of inducing complex recurrent dynamics and modular structure in RNNs. We next contribute a new cognitive multitask battery, Mod-Cog, consisting of up to 132 tasks that expands by about seven-fold the number of tasks and task complexity of 20-Cog-tasks. Importantly, while autapses can solve the simple 20-Cog-tasks, the expanded task set requires richer neural architectures and continuous attractor dynamics. On these tasks, we show that LM-RNNs with an optimal sparsity result in faster training and better data efficiency than fully connected networks.
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Affiliation(s)
- Mikail Khona
- Department of Physics, MIT, Cambridge, MA 02139, U.S.A.
| | - Sarthak Chandra
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
| | - Joy J Ma
- Department of Physics, MIT, Cambridge, MA 02139, U.S.A.
| | - Ila R Fiete
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
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Qin S. Emotion representations in context: maturation and convergence pathways. Trends Cogn Sci 2023; 27:883-885. [PMID: 37598002 DOI: 10.1016/j.tics.2023.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/23/2023] [Indexed: 08/21/2023]
Abstract
How does the human brain develop stable emotion representations? According to recent work by Camacho et al., neural representations of contextualized emotional cues are distinct and fairly stable by mid-to-late childhood and activation patterns become increasingly similar between individuals during adolescence. Here, I propose a framework for investigating contextualized emotion processing.
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Affiliation(s)
- Shaozheng Qin
- IDG/McGovern Institute for Brain Research & State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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Tan PK, Tang C, Herikstad R, Pillay A, Libedinsky C. Distinct Lateral Prefrontal Regions Are Organized in an Anterior-Posterior Functional Gradient. J Neurosci 2023; 43:6564-6572. [PMID: 37607819 PMCID: PMC10513068 DOI: 10.1523/jneurosci.0007-23.2023] [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: 01/03/2023] [Revised: 08/13/2023] [Accepted: 08/15/2023] [Indexed: 08/24/2023] Open
Abstract
The dorsolateral prefrontal cortex (dlPFC) is composed of multiple anatomically defined regions involved in higher-order cognitive processes, including working memory and selective attention. It is organized in an anterior-posterior global gradient where posterior regions track changes in the environment, whereas anterior regions support abstract neural representations. However, it remains unknown if such a global gradient results from a smooth gradient that spans regions or an emergent property arising from functionally distinct regions, that is, an areal gradient. Here, we recorded single neurons in the dlPFC of nonhuman primates trained to perform a memory-guided saccade task with an interfering distractor and analyzed their physiological properties along the anterior-posterior axis. We found that these physiological properties were best described by an areal gradient. Further, population analyses revealed that there is a distributed representation of spatial information across the dlPFC. Our results validate the functional boundaries between anatomically defined dlPFC regions and highlight the distributed nature of computations underlying working memory across the dlPFC.SIGNIFICANCE STATEMENT Activity of frontal lobe regions is known to possess an anterior-posterior functional gradient. However, it is not known whether this gradient is the result of individual brain regions organized in a gradient (like a staircase), or a smooth gradient that spans regions (like a slide). Analysis of physiological properties of individual neurons in the primate frontal regions suggest that individual regions are organized as a gradient, rather than a smooth gradient. At the population level, working memory was more prominent in posterior regions, although it was also present in anterior regions. This is consistent with the functional segregation of brain regions that is also observed in other systems (i.e., the visual system).
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Affiliation(s)
- Pin Kwang Tan
- Institute of Neuroscience, University of Texas at Austin, Austin, Texas 78712
| | - Cheng Tang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Roger Herikstad
- The N1 Institute for Health, National University of Singapore, Singapore 117456
| | - Arunika Pillay
- Department of Psychology, Royal Holloway University of London, Egham TW20 OEX, United Kingdom
| | - Camilo Libedinsky
- The N1 Institute for Health, National University of Singapore, Singapore 117456
- Department of Psychology, National University of Singapore, Singapore 117570
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, National University of Singapore, Singapore 138632
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Miconi T, Kay K. An active neural mechanism for relational learning and fast knowledge reassembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550739. [PMID: 37546842 PMCID: PMC10402151 DOI: 10.1101/2023.07.27.550739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
How do we gain general insights from limited novel experiences? Humans and animals have a striking ability to learn relationships between experienced items, enabling efficient generalization and rapid assimilation of new information. One fundamental instance of such relational learning is transitive inference (learn A>B and B>C, infer A>C), which can be quickly and globally reorganized upon learning a new item (learn A>B>C and D>E>F, then C>D, and infer B>E). Despite considerable study, neural mechanisms of transitive inference and fast reassembly of existing knowledge remain elusive. Here we adopt a meta-learning ("learning-to-learn") approach. We train artificial neural networks, endowed with synaptic plasticity and neuromodulation, to be able to learn novel orderings of arbitrary stimuli from repeated presentation of stimulus pairs. We then obtain a complete mechanistic understanding of this discovered neural learning algorithm. Remarkably, this learning involves active cognition: items from previous trials are selectively reinstated in working memory, enabling delayed, self-generated learning and knowledge reassembly. These findings identify a new mechanism for relational learning and insight, suggest new interpretations of neural activity in cognitive tasks, and highlight a novel approach to discovering neural mechanisms capable of supporting cognitive behaviors.
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Stavropoulos A, Lakshminarasimhan KJ, Angelaki DE. Belief embodiment through eye movements facilitates memory-guided navigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554107. [PMID: 37662309 PMCID: PMC10473632 DOI: 10.1101/2023.08.21.554107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Neural network models optimized for task performance often excel at predicting neural activity but do not explain other properties such as the distributed representation across functionally distinct areas. Distributed representations may arise from animals' strategies for resource utilization, however, fixation-based paradigms deprive animals of a vital resource: eye movements. During a naturalistic task in which humans use a joystick to steer and catch flashing fireflies in a virtual environment lacking position cues, subjects physically track the latent task variable with their gaze. We show this strategy to be true also during an inertial version of the task in the absence of optic flow and demonstrate that these task-relevant eye movements reflect an embodiment of the subjects' dynamically evolving internal beliefs about the goal. A neural network model with tuned recurrent connectivity between oculomotor and evidence-integrating frontoparietal circuits accounted for this behavioral strategy. Critically, this model better explained neural data from monkeys' posterior parietal cortex compared to task-optimized models unconstrained by such an oculomotor-based cognitive strategy. These results highlight the importance of unconstrained movement in working memory computations and establish a functional significance of oculomotor signals for evidence-integration and navigation computations via embodied cognition.
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Affiliation(s)
| | | | - Dora E. Angelaki
- Center for Neural Science, New York University, New York, NY, USA
- Tandon School of Engineering, New York University, New York, NY, USA
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Thrower L, Dang W, Jaffe RG, Sun JD, Constantinidis C. Decoding working memory information from persistent and activity-silent neurons in the primate prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550371. [PMID: 37546782 PMCID: PMC10402050 DOI: 10.1101/2023.07.25.550371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have recently challenged this idea. Activity-silent theories posit that stimulus information may be maintained by the activity pattern of neurons that do not produce firing rate significantly elevated about their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Although averaged across all neurons and stimuli, firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory, this grant average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of information about the stimulus identity. These results reveal that prefrontal neurons with generate persistent activity constitute the primary mechanism of working memory maintenance in the cortex. NEW AND NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. While the two models have been debated on theoretical terms, direct comparison of empirical results have been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
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50
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Liu J, Bayle DJ, Spagna A, Sitt JD, Bourgeois A, Lehongre K, Fernandez-Vidal S, Adam C, Lambrecq V, Navarro V, Seidel Malkinson T, Bartolomeo P. Fronto-parietal networks shape human conscious report through attention gain and reorienting. Commun Biol 2023; 6:730. [PMID: 37454150 PMCID: PMC10349830 DOI: 10.1038/s42003-023-05108-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
How do attention and consciousness interact in the human brain? Rival theories of consciousness disagree on the role of fronto-parietal attentional networks in conscious perception. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Clustering revealed three neural patterns: first, attention-enhanced conscious report accompanied sustained right-hemisphere fronto-temporal activity in networks connected by the superior longitudinal fasciculus (SLF) II-III, and late accumulation of activity (>300 ms post-target) in bilateral dorso-prefrontal and right-hemisphere orbitofrontal cortex (SLF I-III). Second, attentional reorienting affected conscious report through early, sustained activity in a right-hemisphere network (SLF III). Third, conscious report accompanied left-hemisphere dorsolateral-prefrontal activity. Task modeling with recurrent neural networks revealed multiple clusters matching the identified brain clusters, elucidating the causal relationship between clusters in conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience.
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Affiliation(s)
- Jianghao Liu
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
- Dassault Systèmes, Vélizy-Villacoublay, France.
| | | | - Alfredo Spagna
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Department of Psychology, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Jacobo D Sitt
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Alexia Bourgeois
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland
| | - Katia Lehongre
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Sara Fernandez-Vidal
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Claude Adam
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Virginie Lambrecq
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Clinical Neurophysiology Department, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Vincent Navarro
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Clinical Neurophysiology Department, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Tal Seidel Malkinson
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
- CNRS, CRAN, Université de Lorraine, F-54000, Nancy, France.
| | - Paolo Bartolomeo
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
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