1
|
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.
Collapse
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
| |
Collapse
|
2
|
Stroud JP, Duncan J, Lengyel M. The computational foundations of dynamic coding in working memory. Trends Cogn Sci 2024; 28:614-627. [PMID: 38580528 DOI: 10.1016/j.tics.2024.02.011] [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: 12/11/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.
Collapse
Affiliation(s)
- Jake P Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
| |
Collapse
|
3
|
Jensen KT, Hennequin G, Mattar MG. A recurrent network model of planning explains hippocampal replay and human behavior. Nat Neurosci 2024:10.1038/s41593-024-01675-7. [PMID: 38849521 DOI: 10.1038/s41593-024-01675-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 05/07/2024] [Indexed: 06/09/2024]
Abstract
When faced with a novel situation, people often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behavior must compensate for the time spent thinking. Here, we capture these features of behavior by developing a neural network model where planning itself is controlled by the prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences from its own policy, which we call 'rollouts'. In a spatial navigation task, the agent learns to plan when it is beneficial, which provides a normative explanation for empirical variability in human thinking times. Additionally, the patterns of policy rollouts used by the artificial agent closely resemble patterns of rodent hippocampal replays. Our work provides a theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by-and adaptively affect-prefrontal dynamics.
Collapse
Affiliation(s)
- Kristopher T Jensen
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
- Sainsbury Wellcome Centre, University College London, London, UK.
| | - Guillaume Hennequin
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Marcelo G Mattar
- Department of Cognitive Science, University of California, San Diego, CA, USA
- Department of Psychology, New York University, New York, NY, USA
| |
Collapse
|
4
|
Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity that shapes adaptation. Nat Commun 2024; 15:4084. [PMID: 38744847 PMCID: PMC11094149 DOI: 10.1038/s41467-024-48008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
Collapse
Affiliation(s)
- Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Mila, Québec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
| |
Collapse
|
5
|
Sadeghi M, Sharif Razavian R, Bazzi S, Chowdhury RH, Batista AP, Loughlin PJ, Sternad D. Inferring control objectives in a virtual balancing task in humans and monkeys. eLife 2024; 12:RP88514. [PMID: 38738986 PMCID: PMC11090506 DOI: 10.7554/elife.88514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024] Open
Abstract
Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different strategies. Given only observations of behavior, is it possible to infer the control objective that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular strategy. This study presents a three-pronged approach to infer an animal's control objective from behavior. First, both humans and monkeys performed a virtual balancing task for which different control strategies could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that represented two main control objectives to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control objective was being used. Third, these behavioral signatures allowed us to infer the control objective used by human subjects who had been instructed to use one control objective or the other. Based on this validation, we could then infer objectives from animal subjects. Being able to positively identify a subject's control objective from observed behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.
Collapse
Affiliation(s)
- Mohsen Sadeghi
- Department of Biology, Northeastern UniversityBostonUnited States
| | - Reza Sharif Razavian
- Department of Biology, Northeastern UniversityBostonUnited States
- Department of Electrical and Computer Engineering, Northeastern UniversityBostonUnited States
- Department of Mechanical Engineering, Northern Arizona UniversityFlagstaffUnited States
| | - Salah Bazzi
- Department of Biology, Northeastern UniversityBostonUnited States
- Department of Electrical and Computer Engineering, Northeastern UniversityBostonUnited States
- Institute for Experiential Robotics, Northeastern UniversityBostonUnited States
| | - Raeed H Chowdhury
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of PittsburghPittsburghUnited States
| | - Aaron P Batista
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of PittsburghPittsburghUnited States
| | - Patrick J Loughlin
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of PittsburghPittsburghUnited States
| | - Dagmar Sternad
- Department of Biology, Northeastern UniversityBostonUnited States
- Department of Electrical and Computer Engineering, Northeastern UniversityBostonUnited States
- Institute for Experiential Robotics, Northeastern UniversityBostonUnited States
- Department of Physics, Northeastern UniversityBostonUnited States
| |
Collapse
|
6
|
Lakshminarasimhan KJ, Xie M, Cohen JD, Sauerbrei BA, Hantman AW, Litwin-Kumar A, Escola S. Specific connectivity optimizes learning in thalamocortical loops. Cell Rep 2024; 43:114059. [PMID: 38602873 PMCID: PMC11104520 DOI: 10.1016/j.celrep.2024.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 01/04/2024] [Accepted: 03/20/2024] [Indexed: 04/13/2024] Open
Abstract
Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.
Collapse
Affiliation(s)
| | - Marjorie Xie
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Jeremy D Cohen
- Neuroscience Center, University of North Carolina, Chapel Hill, NC 27559, USA
| | - Britton A Sauerbrei
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Adam W Hantman
- Neuroscience Center, University of North Carolina, Chapel Hill, NC 27559, USA
| | - Ashok Litwin-Kumar
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Sean Escola
- Department of Psychiatry, Columbia University, New York, NY 10032, USA.
| |
Collapse
|
7
|
Menéndez JA, Hennig JA, Golub MD, Oby ER, Sadtler PT, Batista AP, Chase SM, Yu BM, Latham PE. A theory of brain-computer interface learning via low-dimensional control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.589952. [PMID: 38712193 PMCID: PMC11071278 DOI: 10.1101/2024.04.18.589952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Chae S, Sohn JW, Kim SP. Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing. J Neurosci 2024; 44:e1353232024. [PMID: 38233217 PMCID: PMC10883619 DOI: 10.1523/jneurosci.1353-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 12/10/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.
Collapse
Affiliation(s)
- Soyoung Chae
- Ulsan National Institute of Science and Technology, Ulsan 44929, South Korea
| | - Jeong-Woo Sohn
- Catholic Kwandong University, Gangwon-do 25601, South Korea
| | - Sung-Phil Kim
- Ulsan National Institute of Science and Technology, Ulsan 44929, South Korea
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. Proc Natl Acad Sci U S A 2023; 120:e2307991120. [PMID: 37983510 PMCID: PMC10691340 DOI: 10.1073/pnas.2307991120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/29/2023] [Indexed: 11/22/2023] Open
Abstract
Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.
Collapse
Affiliation(s)
- Jake P. Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Kei Watanabe
- Graduate School of Frontier Biosciences, Osaka University, Osaka565-0871, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Communication and Information Technology, Osaka565-0871, Japan
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, BudapestH-1051, Hungary
| |
Collapse
|
13
|
Sadeghi M, Razavian RS, Bazzi S, Chowdhury R, Batista A, Loughlin P, Sternad D. Inferring control objectives in a virtual balancing task in humans and monkeys. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.02.539055. [PMID: 37205497 PMCID: PMC10187212 DOI: 10.1101/2023.05.02.539055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different control objectives. Given only observations of behavior, is it possible to infer the control strategy that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular control strategy. This study presents a threepronged approach to infer an animal's control strategy from behavior. First, both humans and monkeys performed a virtual balancing task for which different control objectives could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that represented two main control strategies to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control objective was being used. Third, these behavioral signatures allowed us to infer the control objective used by human subjects who had been instructed to use one control objective or the other. Based on this validation, we could then infer strategies from animal subjects. Being able to positively identify a subject's control objective from behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.
Collapse
Affiliation(s)
- Mohsen Sadeghi
- Department of Biology, Northeastern University
- Department of Electrical and Computer Engineering, Northeastern University
| | | | - Salah Bazzi
- Institute for Experiential Robotics, Northeastern University
| | - Raeed Chowdhury
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA
| | - Aaron Batista
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA
| | - Patrick Loughlin
- Department of Bioengineering, and Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA
| | - Dagmar Sternad
- Department of Biology, Northeastern University
- Department of Electrical and Computer Engineering, Northeastern University
- Institute for Experiential Robotics, Northeastern University
- Department of Physics, Northeastern University
| |
Collapse
|
14
|
Athalye VR, Khanna P, Gowda S, Orsborn AL, Costa RM, Carmena JM. Invariant neural dynamics drive commands to control different movements. Curr Biol 2023; 33:2962-2976.e15. [PMID: 37402376 PMCID: PMC10527529 DOI: 10.1016/j.cub.2023.06.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/24/2023] [Accepted: 06/09/2023] [Indexed: 07/06/2023]
Abstract
It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.
Collapse
Affiliation(s)
- Vivek R Athalye
- Zuckerman Mind Brain Behavior Institute, Departments of Neuroscience and Neurology, Columbia University, New York, NY 10027, USA.
| | - Preeya Khanna
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Suraj Gowda
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Amy L Orsborn
- Departments of Bioengineering, Electrical and Computer Engineering, University of Washington, Seattle, Seattle, WA 98195, USA
| | - Rui M Costa
- Zuckerman Mind Brain Behavior Institute, Departments of Neuroscience and Neurology, Columbia University, New York, NY 10027, USA.
| | - Jose M Carmena
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; UC Berkeley-UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA.
| |
Collapse
|
15
|
Heald JB, Wolpert DM, Lengyel M. The Computational and Neural Bases of Context-Dependent Learning. Annu Rev Neurosci 2023; 46:233-258. [PMID: 36972611 PMCID: PMC10348919 DOI: 10.1146/annurev-neuro-092322-100402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.
Collapse
Affiliation(s)
- James B Heald
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
| | - Daniel M Wolpert
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; ,
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
| |
Collapse
|
16
|
Bachschmid-Romano L, Hatsopoulos NG, Brunel N. Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex. eLife 2023; 12:77690. [PMID: 37166452 PMCID: PMC10174693 DOI: 10.7554/elife.77690] [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: 02/08/2022] [Accepted: 03/09/2023] [Indexed: 05/12/2023] Open
Abstract
The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.
Collapse
Affiliation(s)
| | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, United States
- Committee on Computational Neuroscience, University of Chicago, Chicago, United States
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, United States
- Department of Physics, Duke University, Durham, United States
- Duke Institute for Brain Sciences, Duke University, Durham, United States
- Center for Cognitive Neuroscience, Duke University, Durham, United States
| |
Collapse
|
17
|
Marciniak Dg Agra K, Dg Agra P. F = ma. Is the macaque brain Newtonian? Cogn Neuropsychol 2023; 39:376-408. [PMID: 37045793 DOI: 10.1080/02643294.2023.2191843] [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: 04/14/2023]
Abstract
Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.
Collapse
Affiliation(s)
- Karolina Marciniak Dg Agra
- The Rockefeller University, Laboratory of Neural Circuits, New York, NY, USA
- Center for Brain, Minds and Machines, Cambridge, MA, USA
| | - Pedro Dg Agra
- The Rockefeller University, Laboratory of Neural Circuits, New York, NY, USA
- Center for Brain, Minds and Machines, Cambridge, MA, USA
| |
Collapse
|
18
|
Meirhaeghe N, Riehle A, Brochier T. Parallel movement planning is achieved via an optimal preparatory state in motor cortex. Cell Rep 2023; 42:112136. [PMID: 36807145 DOI: 10.1016/j.celrep.2023.112136] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/16/2022] [Accepted: 02/01/2023] [Indexed: 02/22/2023] Open
Abstract
How do patterns of neural activity in the motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that the motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyze preparatory activity in non-human primates planning not one but two movements simultaneously. As predicted by the theory, we find that parallel planning is achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a trade-off between the two movements. The theory quantitatively accounts for the relationship between this intermediate state and fluctuations in the animals' behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel and further point to motor planning as a controlled dynamical process.
Collapse
Affiliation(s)
- Nicolas Meirhaeghe
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France.
| | - Alexa Riehle
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France; Institute of Neuroscience and Medicine (INM-6), Jülich Research Centre, 52428 Jülich, Germany
| | - Thomas Brochier
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France
| |
Collapse
|
19
|
Machado VG, Brun ABS, Manffra EF. Effects of the radio electric asymmetric conveyer (REAC) on motor disorders: An integrative review. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 5:1122245. [PMID: 36923595 PMCID: PMC10009233 DOI: 10.3389/fmedt.2023.1122245] [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: 12/12/2022] [Accepted: 01/25/2023] [Indexed: 03/03/2023] Open
Abstract
Introduction The radio electric asymmetric conveyer (REAC) is a technology that has the purpose of restoring the cellular polarity triggering the rebalancing of the endogenous bioelectric field, which considering the neurological dysfunctions, affects the neural communication mechanisms. The studies published so far show that the REAC neuromodulation technology has positive effects in treating these dysfunctions, with the principles of endogenous bioelectricity as a basis to achieve these effects. Objectives This study aims to review the literature that explored the effects of REAC protocols on motor control and to identify which mechanisms would be involved. Materials and methods This integrative review considered studies that used REAC as a therapeutic intervention directed at human motor control and experimental research with animals that applied REAC to obtain effects related to motor behavior. Results Ten articles were included, eight clinical and two experimental studies. The clinical studies used the neuro postural optimization (NPO) protocol in 473 patients, of which 53 were healthy subjects, 91 were Alzheimer's disease patients, 128 were patients with atypical swallowing, 12 subjects with neurological diseases, and 189 were without the specification of disease. The experimental studies used the antalgic neuromodulation and neurodegeneration protocols in animal models. Conclusion The information integrated in this review made it possible to consider REAC technology a promising resource for treating motor control dysfunctions. It is possible to infer that the technology promotes functional optimization of neuronal circuits that may be related to more efficient strategies to perform motor tasks.
Collapse
Affiliation(s)
- Vinícius Gomes Machado
- Health Technology Graduate Program, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil
| | | | | |
Collapse
|
20
|
Jensen KT, Kadmon Harpaz N, Dhawale AK, Wolff SBE, Ölveczky BP. Long-term stability of single neuron activity in the motor system. Nat Neurosci 2022; 25:1664-1674. [PMID: 36357811 PMCID: PMC11152193 DOI: 10.1038/s41593-022-01194-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 10/03/2022] [Indexed: 11/12/2022]
Abstract
How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors-both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are generated by single neuron activity patterns that are themselves highly stable.
Collapse
Affiliation(s)
- Kristopher T Jensen
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Naama Kadmon Harpaz
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Ashesh K Dhawale
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Steffen B E Wolff
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
21
|
Ujfalussy BB, Orbán G. Sampling motion trajectories during hippocampal theta sequences. eLife 2022; 11:74058. [DOI: 10.7554/elife.74058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 09/28/2022] [Indexed: 11/06/2022] Open
Abstract
Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here, we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly and efficiently in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.
Collapse
Affiliation(s)
- Balazs B Ujfalussy
- Laboratory of Biological Computation, Institute of Experimental Medicine
- Laboratory of Neuronal Signalling, Institute of Experimental Medicine, Budapest
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, Wigner Research Center for Physics, Budapest
| |
Collapse
|
22
|
Yang W, Tipparaju SL, Chen G, Li N. Thalamus-driven functional populations in frontal cortex support decision-making. Nat Neurosci 2022; 25:1339-1352. [PMID: 36171427 PMCID: PMC9534763 DOI: 10.1038/s41593-022-01171-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/18/2022] [Indexed: 12/02/2022]
Abstract
Neurons in frontal cortex exhibit diverse selectivity representing sensory, motor and cognitive variables during decision-making. The neural circuit basis for this complex selectivity remains unclear. We examined activity mediating a tactile decision in mouse anterior lateral motor cortex in relation to the underlying circuits. Contrary to the notion of randomly mixed selectivity, an analysis of 20,000 neurons revealed organized activity coding behavior. Individual neurons exhibited prototypical response profiles that were repeatable across mice. Stimulus, choice and action were coded nonrandomly by distinct neuronal populations that could be delineated by their response profiles. We related distinct selectivity to long-range inputs from somatosensory cortex, contralateral anterior lateral motor cortex and thalamus. Each input connects to all functional populations but with differing strength. Task selectivity was more strongly dependent on thalamic inputs than cortico-cortical inputs. Our results suggest that the thalamus drives subnetworks within frontal cortex coding distinct features of decision-making. Frontal cortex contains a complex mixture of signals reflecting distinct behavioral and cognitive processes. An analysis of 20,000 neurons during decision-making revealed distinct functional clusters and their activities are driven by the thalamus.
Collapse
Affiliation(s)
- Weiguo Yang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Guang Chen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
23
|
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning. Nat Commun 2022; 13:5163. [PMID: 36056006 PMCID: PMC9440011 DOI: 10.1038/s41467-022-32646-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (Hinput) rather than from changes in local connectivity (Hlocal), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation. As expected, Hinput resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to Hlocal only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.
Collapse
|
24
|
Deng X, Liufu M, Xu J, Yang C, Li Z, Chen J. Understanding implicit and explicit sensorimotor learning through neural dynamics. Front Comput Neurosci 2022; 16:960569. [PMID: 35990367 PMCID: PMC9381967 DOI: 10.3389/fncom.2022.960569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Xueqian Deng
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
- *Correspondence: Xueqian Deng
| | - Mengzhan Liufu
- Institute for Mind and Biology, The University of Chicago, Chicago, IL, United States
- Mengzhan Liufu
| | - Jingyue Xu
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, United States
| | - Chen Yang
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China
| | - Zina Li
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China
| | - Juan Chen
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
- Juan Chen
| |
Collapse
|
25
|
Valente A, Ostojic S, Pillow J. Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. Neural Comput 2022; 34:1871-1892. [PMID: 35896161 DOI: 10.1162/neco_a_01522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/15/2022] [Indexed: 11/04/2022]
Abstract
A large body of work has suggested that neural populations exhibit low-dimensional dynamics during behavior. However, there are a variety of different approaches for modeling low-dimensional neural population activity. One approach involves latent linear dynamical system (LDS) models, in which population activity is described by a projection of low-dimensional latent variables with linear dynamics. A second approach involves low-rank recurrent neural networks (RNNs), in which population activity arises directly from a low-dimensional projection of past activity. Although these two modeling approaches have strong similarities, they arise in different contexts and tend to have different domains of application. Here we examine the precise relationship between latent LDS models and linear low-rank RNNs. When can one model class be converted to the other, and vice versa? We show that latent LDS models can only be converted to RNNs in specific limit cases, due to the non-Markovian property of latent LDS models. Conversely, we show that linear RNNs can be mapped onto LDS models, with latent dimensionality at most twice the rank of the RNN. A surprising consequence of our results is that a partially observed RNN is better represented by an LDS model than by an RNN consisting of only observed units.
Collapse
Affiliation(s)
- Adrian Valente
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure-PSL Research University, 75005 Paris, France
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure-PSL Research University, 75005 Paris, France
| | - Jonathan Pillow
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, U.S.A.
| |
Collapse
|
26
|
Efficient coding of cognitive variables underlies dopamine response and choice behavior. Nat Neurosci 2022; 25:738-748. [PMID: 35668173 DOI: 10.1038/s41593-022-01085-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 04/26/2022] [Indexed: 11/26/2022]
Abstract
Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimize representational costs associated with their processing. In this study, we investigated how reward expectations are affected by features of internal representations by studying behavior and dopaminergic activity while mice make time-based decisions. We show that several possible representations allow a reinforcement learning agent to model animals' overall performance during the task. However, only a small subset of highly compressed representations simultaneously reproduced the co-variability in animals' choice behavior and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely match animals' behavior. These results inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward-based computations.
Collapse
|
27
|
Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings. Curr Opin Neurobiol 2021; 70:163-170. [PMID: 34837752 DOI: 10.1016/j.conb.2021.10.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/21/2022]
Abstract
The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.
Collapse
|
28
|
Kalidindi HT, Cross KP, Lillicrap TP, Omrani M, Falotico E, Sabes PN, Scott SH. Rotational dynamics in motor cortex are consistent with a feedback controller. eLife 2021; 10:e67256. [PMID: 34730516 PMCID: PMC8691841 DOI: 10.7554/elife.67256] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.
Collapse
Affiliation(s)
| | - Kevin P Cross
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
| | - Timothy P Lillicrap
- Centre for Computation, Mathematics and Physics, University College LondonLondonUnited Kingdom
| | - Mohsen Omrani
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant'AnnaPisaItaly
| | - Philip N Sabes
- Department of Physiology, University of California, San FranciscoSan FranciscoUnited States
| | - Stephen H Scott
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
| |
Collapse
|
29
|
De Kock R, Gladhill KA, Ali MN, Joiner WM, Wiener M. How movements shape the perception of time. Trends Cogn Sci 2021; 25:950-963. [PMID: 34531138 PMCID: PMC9991018 DOI: 10.1016/j.tics.2021.08.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 08/07/2021] [Accepted: 08/09/2021] [Indexed: 11/16/2022]
Abstract
In order to keep up with a changing environment, mobile organisms must be capable of deciding both where and when to move. This precision necessitates a strong sense of time, as otherwise we would fail in many of our movement goals. Yet, despite this intrinsic link, only recently have researchers begun to understand how these two features interact. Primarily, two effects have been observed: movements can bias time estimates, but they can also make them more precise. Here we review this literature and propose that both effects can be explained by a Bayesian cue combination framework, in which movement itself affords the most precise representation of time, which can influence perception in either feedforward or active sensing modes.
Collapse
|
30
|
Jazayeri M, Ostojic S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. Curr Opin Neurobiol 2021; 70:113-120. [PMID: 34537579 PMCID: PMC8688220 DOI: 10.1016/j.conb.2021.08.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/16/2022]
Abstract
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here, we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
Collapse
Affiliation(s)
- Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives, INSERM U960, École Normale Supérieure - PSL Research University, 75005, Paris, France.
| |
Collapse
|
31
|
Logiaco L, Abbott LF, Escola S. Thalamic control of cortical dynamics in a model of flexible motor sequencing. Cell Rep 2021; 35:109090. [PMID: 34077721 PMCID: PMC8449509 DOI: 10.1016/j.celrep.2021.109090] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/04/2021] [Accepted: 04/16/2021] [Indexed: 12/26/2022] Open
Abstract
The neural mechanisms that generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. We model the basal ganglia inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise-robust way. Additionally, a single "preparatory" thalamocortical network can produce fast cortical dynamics that support rapid transitions between any pair of learned motifs. If the thalamic units associated with each sequence component are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.
Collapse
Affiliation(s)
- Laureline Logiaco
- Zuckerman Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Institute, Department of Psychiatry, Columbia University, New York, NY 10027, USA.
| | - L F Abbott
- Zuckerman Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sean Escola
- Zuckerman Institute, Department of Psychiatry, Columbia University, New York, NY 10027, USA
| |
Collapse
|