1
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Rampinini A, Balboni I, Golestani N, Berthele R. A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis. Brain Res 2024; 1842:149109. [PMID: 38964704 DOI: 10.1016/j.brainres.2024.149109] [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: 02/28/2024] [Revised: 06/01/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024]
Abstract
Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.
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Affiliation(s)
- Alessandra Rampinini
- Department of Psychology, Faculty of Psychology and Education Science, University of Geneva, Geneva, Switzerland; National Centre for Competence in Research Evolving Language, Switzerland
| | - Irene Balboni
- Department of Psychology, Faculty of Psychology and Education Science, University of Geneva, Geneva, Switzerland; Institute of Multilingualism, University of Fribourg, Fribourg, Switzerland; National Centre for Competence in Research Evolving Language, Switzerland
| | - Narly Golestani
- Department of Psychology, Faculty of Psychology and Education Science, University of Geneva, Geneva, Switzerland; Cognitive Science Hub, University of Vienna, Vienna, Austria; Department of Behavioural and Cognitive Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria; National Centre for Competence in Research Evolving Language, Switzerland
| | - Raphael Berthele
- Institute of Multilingualism, University of Fribourg, Fribourg, Switzerland; National Centre for Competence in Research Evolving Language, Switzerland.
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2
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Kim JZ, Larsen B, Parkes L. Shaping dynamical neural computations using spatiotemporal constraints. Biochem Biophys Res Commun 2024; 728:150302. [PMID: 38968771 DOI: 10.1016/j.bbrc.2024.150302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 07/07/2024]
Abstract
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
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Affiliation(s)
- Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, 14853, USA.
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, USA
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, 08854, USA.
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3
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Nassar MR. Toward a computational role for locus coeruleus/norepinephrine arousal systems. Curr Opin Behav Sci 2024; 59:101407. [PMID: 39070697 PMCID: PMC11280330 DOI: 10.1016/j.cobeha.2024.101407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.
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Affiliation(s)
- M R Nassar
- Brown University, Dept of Neuroscience and Carney Institute for Brain Science
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4
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Miller JA, Constantinidis C. Timescales of learning in prefrontal cortex. Nat Rev Neurosci 2024; 25:597-610. [PMID: 38937654 DOI: 10.1038/s41583-024-00836-8] [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: 06/03/2024] [Indexed: 06/29/2024]
Abstract
The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA.
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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5
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Nau M, Schmid AC, Kaplan SM, Baker CI, Kravitz DJ. Centering cognitive neuroscience on task demands and generalization. Nat Neurosci 2024; 27:1656-1667. [PMID: 39075326 DOI: 10.1038/s41593-024-01711-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/17/2024] [Indexed: 07/31/2024]
Abstract
Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.
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Affiliation(s)
- Matthias Nau
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Alexandra C Schmid
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA
| | - Simon M Kaplan
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Dwight J Kravitz
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA.
- Division of Behavioral and Cognitive Sciences, Directorate for Social, Behavioral, and Economic Sciences, US National Science Foundation, Arlington, VA, USA.
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6
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Bray SR, Wyss LS, Chai C, Lozada ME, Wang B. Adaptive robustness through incoherent signaling mechanisms in a regenerative brain. Cell Rep 2024; 43:114580. [PMID: 39133614 DOI: 10.1016/j.celrep.2024.114580] [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: 06/15/2023] [Revised: 05/08/2024] [Accepted: 07/18/2024] [Indexed: 08/21/2024] Open
Abstract
Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.
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Affiliation(s)
- Samuel R Bray
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Livia S Wyss
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Chew Chai
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Maria E Lozada
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33124, USA
| | - Bo Wang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
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7
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Mininni CJ, Zanutto BS. Constructing neural networks with pre-specified dynamics. Sci Rep 2024; 14:18860. [PMID: 39143351 PMCID: PMC11324765 DOI: 10.1038/s41598-024-69747-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 08/08/2024] [Indexed: 08/16/2024] Open
Abstract
A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm-termed generalised Firing-to-Parameter (gFTP)-provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.
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Affiliation(s)
- Camilo J Mininni
- Instituto de Biología y Medicina Experimental, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.
| | - B Silvano Zanutto
- Instituto de Biología y Medicina Experimental, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
- Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aires, Argentina
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8
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Liu Y, Wang XJ. Flexible gating between subspaces in a neural network model of internally guided task switching. Nat Commun 2024; 15:6497. [PMID: 39090084 PMCID: PMC11294624 DOI: 10.1038/s41467-024-50501-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: 01/12/2024] [Accepted: 07/10/2024] [Indexed: 08/04/2024] Open
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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Affiliation(s)
- Yue Liu
- Center for Neural Science, New York University, New York, NY, 10003, USA
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, 10003, USA.
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9
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Wang P, Guo SJ, Li HJ. Brain imaging of a gamified cognitive flexibility task in young and older adults. Brain Imaging Behav 2024; 18:902-912. [PMID: 38627304 DOI: 10.1007/s11682-024-00883-w] [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] [Accepted: 04/10/2024] [Indexed: 08/31/2024]
Abstract
The study aimed to develop and validate a gamified cognitive flexibility task through brain imaging, and to investigate behavioral and brain activation differences between young and older adults during task performance. Thirty-one young adults (aged 18-35) and 31 older adults (aged 60-80) were included in the present study. All participants underwent fMRI scans while completing the gamified cognitive flexibility task. Results showed that young adults outperformed older adults on the task. The left inferior frontal junction (IFJ), a key region of cognitive flexibility, was significantly activated during the task in both older and young adults. Comparatively, the percent signal change in the left IFJ was stronger in older adults than in young adults. Moreover, older adults demonstrated more precise representations during the task in the left IFJ. Additionally, the left inferior parietal lobule (IPL) and superior parietal lobule in older adults and the left middle frontal gyrus (MFG) and inferior frontal gyrus in young adults were also activated during the task. Psychophysiological interaction analyses showed significant functional connectivity between the left IFJ and the left IPL, as well as the right precuneus in older adults. In young adults, significant functional connectivity was found between the left IFJ and the left MFG, as well as the right angular. The current study provides preliminary evidence for the validity of the gamified cognitive flexibility task through brain imaging. The findings suggest that this task could serve as a reliable tool for assessing cognitive flexibility and for exploring age-related differences of cognitive flexibility in both brain and behavior.
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Affiliation(s)
- Ping Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100101, China
- McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Sheng-Ju Guo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Hui-Jie Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100101, China.
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10
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Courellis HS, Minxha J, Cardenas AR, Kimmel DL, Reed CM, Valiante TA, Salzman CD, Mamelak AN, Fusi S, Rutishauser U. Abstract representations emerge in human hippocampal neurons during inference. Nature 2024; 632:841-849. [PMID: 39143207 PMCID: PMC11338822 DOI: 10.1038/s41586-024-07799-x] [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: 11/30/2023] [Accepted: 07/09/2024] [Indexed: 08/16/2024]
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 generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. 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 performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. 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 behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.
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Affiliation(s)
- Hristos S Courellis
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Juri Minxha
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Araceli R Cardenas
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Daniel L Kimmel
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - C Daniel Salzman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stefano Fusi
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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11
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Fascianelli V, Battista A, Stefanini F, Tsujimoto S, Genovesio A, Fusi S. Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks. Nat Commun 2024; 15:6479. [PMID: 39090091 PMCID: PMC11294567 DOI: 10.1038/s41467-024-50503-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 07/10/2024] [Indexed: 08/04/2024] Open
Abstract
Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two male monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry are associated with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses suggest that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.
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Affiliation(s)
- Valeria Fascianelli
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - Aldo Battista
- Center for Neural Science, New York University, New York, NY, USA
| | - Fabio Stefanini
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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12
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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] [MESH Headings] [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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13
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Costacurta JC, Bhandarkar S, Zoltowski DM, Linderman SW. Structured flexibility in recurrent neural networks via neuromodulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605315. [PMID: 39091788 PMCID: PMC11291173 DOI: 10.1101/2024.07.26.605315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The goal of theoretical neuroscience is to develop models that help us better understand biological intelligence. Such models range broadly in complexity and biological detail. For example, task-optimized recurrent neural networks (RNNs) have generated hypotheses about how the brain may perform various computations, but these models typically assume a fixed weight matrix representing the synaptic connectivity between neurons. From decades of neuroscience research, we know that synaptic weights are constantly changing, controlled in part by chemicals such as neuromodulators. In this work we explore the computational implications of synaptic gain scaling, a form of neuromodulation, using task-optimized low-rank RNNs. In our neuromodulated RNN (NM-RNN) model, a neuromodulatory subnetwork outputs a low-dimensional neuromodulatory signal that dynamically scales the low-rank recurrent weights of an output-generating RNN. In empirical experiments, we find that the structured flexibility in the NM-RNN allows it to both train and generalize with a higher degree of accuracy than low-rank RNNs on a set of canonical tasks. Additionally, via theoretical analyses we show how neuromodulatory gain scaling endows networks with gating mechanisms commonly found in artificial RNNs. We end by analyzing the low-rank dynamics of trained NM-RNNs, to show how task computations are distributed.
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Affiliation(s)
- Julia C Costacurta
- Wu Tsai Neurosciences Institute, Stanford, CA, USA
- Department of Electrical Engineering, Stanford, CA, USA
| | | | - David M Zoltowski
- Wu Tsai Neurosciences Institute, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Scott W Linderman
- Wu Tsai Neurosciences Institute, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
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14
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Gu S, Mattar MG, Tang H, Pan G. Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection. SCIENCE ADVANCES 2024; 10:eadm8430. [PMID: 39058783 PMCID: PMC11277393 DOI: 10.1126/sciadv.adm8430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.
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Affiliation(s)
- Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Marcelo G. Mattar
- Department of Psychology, New York University, New York, NY 10003, USA
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
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15
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Serrano-Fernández L, Beirán M, Parga N. Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks. Cell Rep 2024; 43:114412. [PMID: 38968075 DOI: 10.1016/j.celrep.2024.114412] [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: 08/23/2023] [Revised: 04/08/2024] [Accepted: 06/12/2024] [Indexed: 07/07/2024] Open
Abstract
A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.
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Affiliation(s)
- Luis Serrano-Fernández
- Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain; Centro de Investigación Avanzada en Física Fundamental, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Manuel Beirán
- Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Néstor Parga
- Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain; Centro de Investigación Avanzada en Física Fundamental, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
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16
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Zhu V, Rosenbaum R. Learning Fixed Points of Recurrent Neural Networks by Reparameterizing the Network Model. Neural Comput 2024; 36:1568-1600. [PMID: 39028956 DOI: 10.1162/neco_a_01681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/18/2024] [Indexed: 07/21/2024]
Abstract
In computational neuroscience, recurrent neural networks are widely used to model neural activity and learning. In many studies, fixed points of recurrent neural networks are used to model neural responses to static or slowly changing stimuli, such as visual cortical responses to static visual stimuli. These applications raise the question of how to train the weights in a recurrent neural network to minimize a loss function evaluated on fixed points. In parallel, training fixed points is a central topic in the study of deep equilibrium models in machine learning. A natural approach is to use gradient descent on the Euclidean space of weights. We show that this approach can lead to poor learning performance due in part to singularities that arise in the loss surface. We use a reparameterization of the recurrent network model to derive two alternative learning rules that produce more robust learning dynamics. We demonstrate that these learning rules avoid singularities and learn more effectively than standard gradient descent. The new learning rules can be interpreted as steepest descent and gradient descent, respectively, under a non-Euclidean metric on the space of recurrent weights. Our results question the common, implicit assumption that learning in the brain should be expected to follow the negative Euclidean gradient of synaptic weights.
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Affiliation(s)
- Vicky Zhu
- Babson College, Mathematics, Analytics, Science, and Technology Division, Wellesley, MA 02481, U.S.A.
| | - Robert Rosenbaum
- University of Notre Dame, Department of Applied and Computational Mathematics and Statistics, Notre Dame, IN 46556, U.S.A.
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17
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Leite A, Adeli H, McPeek RM, Zelinsky GJ. Evaluating theories of neural information integration during visual search. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601936. [PMID: 39005469 PMCID: PMC11245033 DOI: 10.1101/2024.07.03.601936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
The brain routes and integrates information from many sources during behavior. A number of models explain this phenomenon within the framework of mixed selectivity theory, yet it is difficult to compare their predictions to understand how neurons and circuits integrate information. In this work, we apply time-series partial information decomposition [PID] to compare models of integration on a dataset of superior colliculus [SC] recordings collected during a multi-target visual search task. On this task, SC must integrate target guidance, bottom-up salience, and previous fixation signals to drive attention. We find evidence that SC neurons integrate these factors in diverse ways, including decision-variable selectivity to expected value, functional specialization to previous fixation, and code-switching (to incorporate new visual input).
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Affiliation(s)
- Abe Leite
- Departments of Psychology and Computer Science, Stony Brook University, New York, USA
| | - Hossein Adeli
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
| | - Robert M McPeek
- Department of Biological and Vision Sciences, SUNY College of Optometry, New York, USA
| | - Gregory J Zelinsky
- Departments of Psychology and Computer Science, Stony Brook University, New York, USA
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18
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Ostojic S, Fusi S. Computational role of structure in neural activity and connectivity. Trends Cogn Sci 2024; 28:677-690. [PMID: 38553340 DOI: 10.1016/j.tics.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 07/05/2024]
Abstract
One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.
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Affiliation(s)
- Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005 Paris, France.
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA
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19
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Rao RPN. A sensory-motor theory of the neocortex. Nat Neurosci 2024; 27:1221-1235. [PMID: 38937581 DOI: 10.1038/s41593-024-01673-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 04/26/2024] [Indexed: 06/29/2024]
Abstract
Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.
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Affiliation(s)
- Rajesh P N Rao
- Center for Neurotechnology, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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20
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Driscoll LN, Shenoy K, Sussillo D. Flexible multitask computation in recurrent networks utilizes shared dynamical motifs. Nat Neurosci 2024; 27:1349-1363. [PMID: 38982201 PMCID: PMC11239504 DOI: 10.1038/s41593-024-01668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 04/26/2024] [Indexed: 07/11/2024]
Abstract
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.
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Affiliation(s)
- Laura N Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Krishna Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, 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
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
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21
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Kumar S, Sumers TR, Yamakoshi T, Goldstein A, Hasson U, Norman KA, Griffiths TL, Hawkins RD, Nastase SA. Shared functional specialization in transformer-based language models and the human brain. Nat Commun 2024; 15:5523. [PMID: 38951520 PMCID: PMC11217339 DOI: 10.1038/s41467-024-49173-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 05/24/2024] [Indexed: 07/03/2024] Open
Abstract
When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations ("embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized "transformations" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space.
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Affiliation(s)
- Sreejan Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA.
| | - Theodore R Sumers
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
| | - Takateru Yamakoshi
- Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Ariel Goldstein
- Department of Cognitive and Brain Sciences and Business School, Hebrew University, Jerusalem, 9190401, Israel
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
| | - Thomas L Griffiths
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
| | - Robert D Hawkins
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA.
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22
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Ma T, Hermundstad AM. A vast space of compact strategies for effective decisions. SCIENCE ADVANCES 2024; 10:eadj4064. [PMID: 38905348 PMCID: PMC11192086 DOI: 10.1126/sciadv.adj4064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 05/15/2024] [Indexed: 06/23/2024]
Abstract
Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.
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Affiliation(s)
- Tzuhsuan Ma
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ann M. Hermundstad
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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23
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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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24
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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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25
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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] [Grants] [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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26
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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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27
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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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28
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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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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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30
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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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31
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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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32
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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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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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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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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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36
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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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37
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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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38
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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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39
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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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40
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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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41
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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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42
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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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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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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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45
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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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46
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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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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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48
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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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49
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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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50
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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: 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: 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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