1
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Rodriguez AC, Perich MG, Miller L, Humphries MD. Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.26.542452. [PMID: 37292834 PMCID: PMC10246015 DOI: 10.1101/2023.05.26.542452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity.
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Affiliation(s)
| | - Matthew G. Perich
- Département de neurosciences, Faculté de médecine, Université de Montréal, Montréal, Canada
- Québec Artificial Intelligence Institute (Mila), Québec, Canada
| | - Lee Miller
- Northwestern University, Department of Biomedical Engineering, Chicago, USA
| | - Mark D. Humphries
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
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2
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity that shapes adaptation. Nat Commun 2024; 15:4084. [PMID: 38744847 PMCID: PMC11094149 DOI: 10.1038/s41467-024-48008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
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Affiliation(s)
- Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Mila, Québec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
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3
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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.
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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
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4
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Fortunato C, Bennasar-Vázquez J, Park J, Chang JC, Miller LE, Dudman JT, Perich MG, Gallego JA. Nonlinear manifolds underlie neural population activity during behaviour. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549575. [PMID: 37503015 PMCID: PMC10370078 DOI: 10.1101/2023.07.18.549575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
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Affiliation(s)
- Cátia Fortunato
- Department of Bioengineering, Imperial College London, London UK
| | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Joanna C. Chang
- Department of Bioengineering, Imperial College London, London UK
| | - Lee E. Miller
- Department of Neurosciences, Northwestern University, Chicago IL, USA
- Department of Biomedical Engineering, Northwestern University, Chicago IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago IL, USA, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T. Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Matthew G. Perich
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada
- Québec Artificial Intelligence Institute (MILA), Montréal, Québec, Canada
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London UK
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5
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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.
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6
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Abbasi A, Rangwani R, Bowen DW, Fealy AW, Danielsen NP, Gulati T. Cortico-cerebellar coordination facilitates neuroprosthetic control. SCIENCE ADVANCES 2024; 10:eadm8246. [PMID: 38608024 PMCID: PMC11014440 DOI: 10.1126/sciadv.adm8246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/11/2024] [Indexed: 04/14/2024]
Abstract
Temporally coordinated neural activity is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence demonstrating how this coordinated activity within cortical and subcortical regions governs behavior. We investigated this between the primary motor (M1) and contralateral cerebellar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task. In neuroprosthetic task, actuator movements are causally linked to M1 "direct" neurons that drive the decoder for successful task execution. However, it is unknown how task-related M1 activity interacts with the cerebellum. We observed a notable 3 to 6 hertz coherence that emerged between these regions' local field potentials (LFPs) with learning that also modulated task-related spiking. We identified robust task-related indirect modulation in the cerebellum, which developed a preferential relationship with M1 task-related activity. Inhibiting cerebellar cortical and deep nuclei activity through optogenetics led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that cerebellar influence is necessary for M1-driven neuroprosthetic control.
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Affiliation(s)
- Aamir Abbasi
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rohit Rangwani
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Bioengineering Graduate Program, Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California-Los Angeles, CA, USA
| | - Daniel W. Bowen
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew W. Fealy
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nathan P. Danielsen
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tanuj Gulati
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Bioengineering Graduate Program, Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California-Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, and Department of Bioengineering, Henry Samueli School of Engineering, University of California-Los Angeles, Los Angeles, CA, USA
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7
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Subramoney A, Bellec G, Scherr F, Legenstein R, Maass W. Fast learning without synaptic plasticity in spiking neural networks. Sci Rep 2024; 14:8557. [PMID: 38609429 PMCID: PMC11015027 DOI: 10.1038/s41598-024-55769-0] [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: 06/08/2023] [Accepted: 02/27/2024] [Indexed: 04/14/2024] Open
Abstract
Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.
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Affiliation(s)
- Anand Subramoney
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
- Department of Computer Science, Royal Holloway University of London, Egham, UK
| | - Guillaume Bellec
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
- Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Franz Scherr
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Robert Legenstein
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Wolfgang Maass
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
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8
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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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9
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Gmaz JM, Keller JA, Dudman JT, Gallego JA. Integrating across behaviors and timescales to understand the neural control of movement. Curr Opin Neurobiol 2024; 85:102843. [PMID: 38354477 DOI: 10.1016/j.conb.2024.102843] [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: 08/03/2023] [Revised: 12/03/2023] [Accepted: 01/13/2024] [Indexed: 02/16/2024]
Abstract
The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.
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Affiliation(s)
- Jimmie M Gmaz
- Department of Bioengineering, Imperial College London, London, UK. https://twitter.com/j_gmaz
| | - Jason A Keller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA. https://twitter.com/jakNeurd
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA.
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
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10
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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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11
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Rajeswaran P, Payeur A, Lajoie G, Orsborn AL. Assistive sensory-motor perturbations influence learned neural representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585972. [PMID: 38562772 PMCID: PMC10983972 DOI: 10.1101/2024.03.20.585972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
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Affiliation(s)
| | - Alexandre Payeur
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Guillaume Lajoie
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Amy L. Orsborn
- University of Washington, Bioengineering, Seattle, 98115, USA
- University of Washington, Electrical and Computer Engineering, Seattle, 98115, USA
- Washington National Primate Research Center, Seattle, Washington, 98115, USA
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12
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Weng G, Clark K, Akbarian A, Noudoost B, Nategh N. Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas. Front Comput Neurosci 2024; 18:1273053. [PMID: 38348287 PMCID: PMC10859875 DOI: 10.3389/fncom.2024.1273053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
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Affiliation(s)
- Geyu Weng
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Amir Akbarian
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Neda Nategh
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
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13
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Gurnani H, Cayco Gajic NA. Signatures of task learning in neural representations. Curr Opin Neurobiol 2023; 83:102759. [PMID: 37708653 DOI: 10.1016/j.conb.2023.102759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Abstract
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.
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Affiliation(s)
- Harsha Gurnani
- Department of Biology, University of Washington, Seattle, WA, USA. https://twitter.com/HarshaGurnani
| | - N Alex Cayco Gajic
- Laboratoire de Neuroscience Cognitives, Ecole Normale Supérieure, Université PSL, Paris, France.
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14
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Safaie M, Chang JC, Park J, Miller LE, Dudman JT, Perich MG, Gallego JA. Preserved neural dynamics across animals performing similar behaviour. Nature 2023; 623:765-771. [PMID: 37938772 PMCID: PMC10665198 DOI: 10.1038/s41586-023-06714-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/04/2023] [Indexed: 11/09/2023]
Abstract
Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.
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Affiliation(s)
- Mostafa Safaie
- Department of Bioengineering, Imperial College London, London, UK
| | - Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada.
- Mila, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada.
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
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15
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Barbosa J, Proville R, Rodgers CC, DeWeese MR, Ostojic S, Boubenec Y. Early selection of task-relevant features through population gating. Nat Commun 2023; 14:6837. [PMID: 37884507 PMCID: PMC10603060 DOI: 10.1038/s41467-023-42519-5] [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: 10/24/2022] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.
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Affiliation(s)
- Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France.
| | - Rémi Proville
- Tailored Data Solutions, 192 Cours Gambetta, 84300, Cavaillon, France
| | - Chris C Rodgers
- Department of Neurosurgery, Emory University, Atlanta, GA, 30033, USA
| | - Michael R DeWeese
- Department of Physics, Helen Wills Neuroscience Institute, and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France
| | - Yves Boubenec
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure PSL Research University, CNRS, Paris, France
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16
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Tian LY, Warren TL, Mehaffey WH, Brainard MS. Dynamic top-down biasing implements rapid adaptive changes to individual movements. eLife 2023; 12:e83223. [PMID: 37733005 PMCID: PMC10513479 DOI: 10.7554/elife.83223] [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: 09/03/2022] [Accepted: 09/11/2023] [Indexed: 09/22/2023] Open
Abstract
Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN (lateral magnocellular nucleus of the anterior nidopallium), in shaping moment-by-moment output from a primary motor area, RA (robust nucleus of the arcopallium). In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation in activity between these areas. During learning, this co-fluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.
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Affiliation(s)
- Lucas Y Tian
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San FranciscoSan FranciscoUnited States
| | - Timothy L Warren
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San FranciscoSan FranciscoUnited States
| | - William H Mehaffey
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San FranciscoSan FranciscoUnited States
| | - Michael S Brainard
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San FranciscoSan FranciscoUnited States
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17
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Dong Y, Wang S, Huang Q, Berg RW, Li G, He J. Neural Decoding for Intracortical Brain-Computer Interfaces. CYBORG AND BIONIC SYSTEMS 2023; 4:0044. [PMID: 37519930 PMCID: PMC10380541 DOI: 10.34133/cbsystems.0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.
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Affiliation(s)
- Yuanrui Dong
- School of Mechatronical Engineering and Beijing Advanced Innovation Center for Intelligent Robots,
Beijing Institute of Technology, Beijing 100081, China
| | - Shirong Wang
- School of Mechatronical Engineering and Beijing Advanced Innovation Center for Intelligent Robots,
Beijing Institute of Technology, Beijing 100081, China
| | - Qiang Huang
- School of Mechatronical Engineering and Beijing Advanced Innovation Center for Intelligent Robots,
Beijing Institute of Technology, Beijing 100081, China
| | - Rune W. Berg
- Department of Neuroscience,
University of Copenhagen, Copenhagen 2200, Denmark
| | - Guanghui Li
- Department of Neuroscience,
University of Copenhagen, Copenhagen 2200, Denmark
| | - Jiping He
- School of Mechatronical Engineering and Beijing Advanced Innovation Center for Intelligent Robots,
Beijing Institute of Technology, Beijing 100081, China
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18
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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.
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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.
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19
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity space that shapes adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541925. [PMID: 37293081 PMCID: PMC10245862 DOI: 10.1101/2023.05.23.541925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during de novo learning and subsequent adaptation using recurrent neural networks. We trained these networks on different motor repertoires comprising varying numbers of movements. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization created by the neural population activity patterns corresponding to each movement. This structure facilitated adaptation, but only when small changes in motor output were required, and when the structure of the network inputs, the neural activity space, and the perturbation were congruent. These results highlight trade-offs in skill acquisition and demonstrate how prior experience and external cues during learning can shape the geometrical properties of neural population activity as well as subsequent adaptation.
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Affiliation(s)
- Joanna C. Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G. Perich
- Département de neurosciences, Université de Montréal, Montréal, Canada
| | - Lee E. Miller
- Department of Neuroscience, Northwestern University, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London, UK
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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20
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Nakuci J, Wasylyshyn N, Cieslak M, Elliott JC, Bansal K, Giesbrecht B, Grafton ST, Vettel JM, Garcia JO, Muldoon SF. Within-subject reproducibility varies in multi-modal, longitudinal brain networks. Sci Rep 2023; 13:6699. [PMID: 37095180 PMCID: PMC10126005 DOI: 10.1038/s41598-023-33441-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, 14260, USA.
| | - Nick Wasylyshyn
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - James C Elliott
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Kanika Bansal
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Jean M Vettel
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Javier O Garcia
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah F Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
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21
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Borgognon S, Rouiller EM. Loss of Motor Cortical Inputs to the Red Nucleus after CNS Disorders in Nonhuman Primates. J Neurosci 2023; 43:1682-1691. [PMID: 36693756 PMCID: PMC10010457 DOI: 10.1523/jneurosci.1942-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/14/2022] [Accepted: 01/13/2023] [Indexed: 01/25/2023] Open
Abstract
The premotor (PM) and primary motor (M1) cortical areas broadcast voluntary motor commands through multiple neuronal pathways, including the corticorubral projection that reaches the red nucleus (RN). However, the respective contribution of M1 and PM to corticorubral projections as well as changes induced by motor disorders or injuries are not known in nonhuman primates. Here, we quantified the density and topography of axonal endings of the corticorubral pathway in RN in intact monkeys, as well as in monkeys subjected to either cervical spinal cord injury (SCI), Parkinson's disease (PD)-like symptoms or primary motor cortex injury (MCI). Twenty adult macaque monkeys of either sex were injected with the biotinylated dextran amine anterograde tracer either in PM or in M1. We developed a semiautomated algorithm to reliably detect and count axonal boutons within the magnocellular and parvocellular (pRN) subdivisions of RN. In intact monkeys, PM and M1 preferentially target the medial part of the ipsilateral pRN, reflecting its somatotopic organization. Projection of PM to the ipsilateral pRN is denser than that of M1, matching previous observations for the corticotectal, corticoreticular, and corticosubthalamic projections (Fregosi et al., 2018, 2019; Borgognon et al., 2020). In all three types of motor disorders, there was a uniform and strong decrease (near loss) of the corticorubral projections from PM and M1. The RN may contribute to functional recovery after SCI, PD, and MCI, by reducing direct cortical influence. This reduction possibly privileges direct access to the final output motor system, via emphasis on the direct corticospinal projection.SIGNIFICANCE STATEMENT We measured the corticorubral projection density arising from the PM or the M1 cortices in adult macaques. The premotor cortex sent denser corticorubral projections than the primary motor cortex, as previously observed for the corticotectal, corticoreticular, and corticosubthalamic projections. The premotor cortex may thus exert more influence than primary motor cortex onto subcortical structures. We next asked whether the corticorubral motor projections undergo lesion-dependent plasticity after either cervical spinal cord injury, Parkinson's disease-like symptoms, or primary motor cortex lesion. In all three types of pathology, there was a strong decrease of the corticorubral motor projection density, suggesting that the red nucleus may contribute to functional recovery after such motor system disorders based on a reduced direct cortical influence.
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Affiliation(s)
- Simon Borgognon
- Center for the Neural Basis of Cognition, Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
- Department of Neurosciences and Movement Sciences, Section of Medicine, Faculty of Science and Medicine, University of Fribourg, CH-1700 Fribourg, Switzerland
- Center for Neuroprosthetics and Brain Mind Institute, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Eric M Rouiller
- Department of Neurosciences and Movement Sciences, Section of Medicine, Faculty of Science and Medicine, University of Fribourg, CH-1700 Fribourg, Switzerland
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22
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Cortico-cortical drive in a coupled premotor-primary motor cortex dynamical system. Cell Rep 2022; 41:111849. [PMID: 36543147 DOI: 10.1016/j.celrep.2022.111849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/13/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
In the conventional view of sensorimotor control, the premotor cortex (PM) plans actions that are executed by the primary motor cortex (M1). This notion arises in part from many experiments that have imposed a preparatory "planning" period, during which PM becomes active without M1. But during many natural movements, PM and M1 are co-activated, making it difficult to distinguish their functional roles. We leverage coupled dynamical systems models (cDSMs) to uncover interactions between PM and M1 during movements performed with no preparatory period. We build cDSMs using neural and behavioral data recorded from two non-human primates as they performed a reach-grasp-manipulate task. PM and M1 interact dynamically throughout these movements. Whereas PM drives the M1 in some situations, in other situations, M1 drives PM activity, contrary to the conventional assumption. Our DSM framework provides additional predictions differentiating the roles of PM and M1 in controlling movement.
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23
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Melbaum S, Russo E, Eriksson D, Schneider A, Durstewitz D, Brox T, Diester I. Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding. Nat Commun 2022; 13:7420. [PMID: 36456557 PMCID: PMC9715555 DOI: 10.1038/s41467-022-35115-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 11/17/2022] [Indexed: 12/04/2022] Open
Abstract
Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior-posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code.
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Affiliation(s)
- Svenja Melbaum
- grid.5963.9Computer Vision Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany ,grid.5963.9IMBIT//BrainLinks-BrainTools, University of Freiburg, Georges-Köhler-Allee 201, 79110 Freiburg, Germany
| | - Eleonora Russo
- grid.410607.4Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany ,grid.7700.00000 0001 2190 4373Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159 Mannheim, Germany
| | - David Eriksson
- grid.5963.9IMBIT//BrainLinks-BrainTools, University of Freiburg, Georges-Köhler-Allee 201, 79110 Freiburg, Germany ,grid.5963.9Optophysiology Lab, Faculty of Biology, University of Freiburg, 79110 Freiburg, Germany
| | - Artur Schneider
- grid.5963.9IMBIT//BrainLinks-BrainTools, University of Freiburg, Georges-Köhler-Allee 201, 79110 Freiburg, Germany ,grid.5963.9Optophysiology Lab, Faculty of Biology, University of Freiburg, 79110 Freiburg, Germany
| | - Daniel Durstewitz
- grid.7700.00000 0001 2190 4373Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159 Mannheim, Germany
| | - Thomas Brox
- grid.5963.9Computer Vision Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany ,grid.5963.9IMBIT//BrainLinks-BrainTools, University of Freiburg, Georges-Köhler-Allee 201, 79110 Freiburg, Germany
| | - Ilka Diester
- grid.5963.9IMBIT//BrainLinks-BrainTools, University of Freiburg, Georges-Köhler-Allee 201, 79110 Freiburg, Germany ,grid.5963.9Optophysiology Lab, Faculty of Biology, University of Freiburg, 79110 Freiburg, Germany ,grid.5963.9Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
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24
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Keshtkaran MR, Sedler AR, Chowdhury RH, Tandon R, Basrai D, Nguyen SL, Sohn H, Jazayeri M, Miller LE, Pandarinath C. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nat Methods 2022; 19:1572-1577. [PMID: 36443486 PMCID: PMC9825111 DOI: 10.1038/s41592-022-01675-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 10/14/2022] [Indexed: 11/30/2022]
Abstract
Achieving state-of-the-art performance with deep neural population dynamics models requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning framework that automatically produces high-performing autoencoding models on data from a variety of brain areas and tasks, without behavioral or task information. We demonstrate its broad applicability on several rhesus macaque datasets: from motor cortex during free-paced reaching, somatosensory cortex during reaching with perturbations, and dorsomedial frontal cortex during a cognitive timing task.
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Affiliation(s)
- Mohammad Reza Keshtkaran
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Andrew R Sedler
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA
| | - Raeed H Chowdhury
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Raghav Tandon
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diya Basrai
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Physiology and Neuroscience, University of California, San Diego, La Jolla, CA, USA
| | - Sarah L Nguyen
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hansem Sohn
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mehrdad Jazayeri
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
- Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
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25
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De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex. eNeuro 2022; 9:ENEURO.0145-22.2022. [PMID: 36376067 PMCID: PMC9721308 DOI: 10.1523/eneuro.0145-22.2022] [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: 04/04/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/15/2022] Open
Abstract
Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.
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26
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Transition of distinct context-dependent ensembles from secondary to primary motor cortex in skilled motor performance. Cell Rep 2022; 41:111494. [DOI: 10.1016/j.celrep.2022.111494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/27/2022] [Accepted: 09/21/2022] [Indexed: 11/19/2022] Open
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27
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Awasthi P, Lin TH, Bae J, Miller LE, Danziger ZC. Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces. J Neural Eng 2022; 19:056038. [PMID: 36198278 PMCID: PMC9855658 DOI: 10.1088/1741-2552/ac97c3] [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: 03/21/2022] [Accepted: 10/05/2022] [Indexed: 01/26/2023]
Abstract
Objective. Despite the tremendous promise of invasive brain-computer interfaces (iBCIs), the associated study costs, risks, and ethical considerations limit the opportunity to develop and test the algorithms that decode neural activity into a user's intentions. Our goal was to address this challenge by designing an iBCI model capable of testing many human subjects in closed-loop.Approach. We developed an iBCI model that uses artificial neural networks (ANNs) to translate human finger movements into realistic motor cortex firing patterns, which can then be decoded in real time. We call the model the joint angle BCI, or jaBCI. jaBCI allows readily recruited, healthy subjects to perform closed-loop iBCI tasks using any neural decoder, preserving subjects' control-relevant short-latency error correction and learning dynamics.Main results. We validated jaBCI offline through emulated neuron firing statistics, confirming that emulated neural signals have firing rates, low-dimensional PCA geometry, and rotational jPCA dynamics that are quite similar to the actual neurons (recorded in monkey M1) on which we trained the ANN. We also tested jaBCI in closed-loop experiments, our single study examining roughly as many subjects as have been tested world-wide with iBCIs (n= 25). Performance was consistent with that of the paralyzed, human iBCI users with implanted intracortical electrodes. jaBCI allowed us to imitate the experimental protocols (e.g. the same velocity Kalman filter decoder and center-out task) and compute the same seven behavioral measures used in three critical studies.Significance. These encouraging results suggest the jaBCI's real-time firing rate emulation is a useful means to provide statistically robust sample sizes for rapid prototyping and optimization of decoding algorithms, the study of bi-directional learning in iBCIs, and improving iBCI control.
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Affiliation(s)
- Peeyush Awasthi
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia
| | - Tzu-Hsiang Lin
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia
| | - Jihye Bae
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, United States
| | - Lee E Miller
- Department of Neuroscience, Physical Medicine, and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Zachary C Danziger
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia,Author to whom any correspondence should be addressed
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28
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Warriner CL, Fageiry S, Saxena S, Costa RM, Miri A. Motor cortical influence relies on task-specific activity covariation. Cell Rep 2022; 40:111427. [PMID: 36170841 PMCID: PMC9536049 DOI: 10.1016/j.celrep.2022.111427] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 03/01/2022] [Accepted: 09/08/2022] [Indexed: 11/18/2022] Open
Abstract
During limb movement, spinal circuits facilitate the alternating activation of antagonistic flexor and extensor muscles. Yet antagonist cocontraction is often required to stabilize joints, like when loads are handled. Previous results suggest that these different muscle activation patterns are mediated by separate flexion- and extension-related motor cortical output populations, while others suggest recruitment of task-specific populations. To distinguish between hypotheses, we developed a paradigm in which mice toggle between forelimb tasks requiring antagonist alternation or cocontraction and measured activity in motor cortical layer 5b. Our results conform to neither hypothesis: consistent flexion- and extension-related activity is not observed across tasks, and no task-specific populations are observed. Instead, activity covariation among motor cortical neurons dramatically changes between tasks, thereby altering the relation between neural and muscle activity. This is also observed specifically for corticospinal neurons. Collectively, our findings indicate that motor cortex drives different muscle activation patterns via task-specific activity covariation.
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Affiliation(s)
- Claire L Warriner
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Samaher Fageiry
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Shreya Saxena
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Grossman Center for Statistics of the Mind, Columbia University, New York, NY 10027, USA
| | - Rui M Costa
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Andrew Miri
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA.
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29
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Zippi EL, You AK, Ganguly K, Carmena JM. Selective modulation of cortical population dynamics during neuroprosthetic skill learning. Sci Rep 2022; 12:15948. [PMID: 36153356 PMCID: PMC9509316 DOI: 10.1038/s41598-022-20218-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/09/2022] [Indexed: 01/23/2023] Open
Abstract
Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.
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Affiliation(s)
- Ellen L. Zippi
- grid.47840.3f0000 0001 2181 7878Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720 USA
| | - Albert K. You
- grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720 USA
| | - Karunesh Ganguly
- grid.410372.30000 0004 0419 2775Neurology and Rehabilitation Service, San Francisco VA Medical Center, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Department of Neurology, University of California, San Francisco, CA 94143 USA
| | - Jose M. Carmena
- grid.47840.3f0000 0001 2181 7878Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720 USA ,grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720 USA
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30
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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.
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31
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Gallego-Carracedo C, Perich MG, Chowdhury RH, Miller LE, Gallego JÁ. Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner. eLife 2022; 11:73155. [PMID: 35968845 PMCID: PMC9470163 DOI: 10.7554/elife.73155] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, the 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.
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Affiliation(s)
| | - Matthew G Perich
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Raeed H Chowdhury
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, United States
| | - Juan Álvaro Gallego
- Department of Bioengineering, Imperial College London, London, United Kingdom
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32
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Donegan D, Kanzler CM, Büscher J, Viskaitis P, Bracey EF, Lambercy O, Burdakov D. Hypothalamic Control of Forelimb Motor Adaptation. J Neurosci 2022; 42:6243-6257. [PMID: 35790405 PMCID: PMC9374158 DOI: 10.1523/jneurosci.0705-22.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 06/12/2022] [Indexed: 11/21/2022] Open
Abstract
The ability to perform skilled arm movements is central to everyday life, as limb impairments in common neurologic disorders such as stroke demonstrate. Skilled arm movements require adaptation of motor commands based on discrepancies between desired and actual movements, called sensory errors. Studies in humans show that this involves predictive and reactive movement adaptations to the errors, and also requires a general motivation to move. How these distinct aspects map onto defined neural signals remains unclear, because of a shortage of equivalent studies in experimental animal models that permit neural-level insights. Therefore, we adapted robotic technology used in human studies to mice, enabling insights into the neural underpinnings of motivational, reactive, and predictive aspects of motor adaptation. Here, we show that forelimb motor adaptation is regulated by neurons previously implicated in motivation and arousal, but not in forelimb motor control: the hypothalamic orexin/hypocretin neurons (HONs). By studying goal-oriented mouse-robot interactions in male mice, we found distinct HON signals occur during forelimb movements and motor adaptation. Temporally-delimited optosilencing of these movement-associated HON signals impaired sensory error-based motor adaptation. Unexpectedly, optosilencing affected neither task reward or execution rates, nor motor performance in tasks that did not require adaptation, indicating that the temporally-defined HON signals studied here were distinct from signals governing general task engagement or sensorimotor control. Collectively, these results reveal a hypothalamic neural substrate regulating forelimb motor adaptation.SIGNIFICANCE STATEMENT The ability to perform skilled, adaptable movements is a fundamental part of daily life, and is impaired in common neurologic diseases such as stroke. Maintaining motor adaptation is thus of great interest, but the necessary brain components remain incompletely identified. We found that impaired motor adaptation results from disruption of cells not previously implicated in this pathology: hypothalamic orexin/hypocretin neurons (HONs). We show that temporally confined HON signals are associated with skilled movements. Without these newly-identified signals, a resistance to movement that is normally rapidly overcome leads to prolonged movement impairment. These results identify natural brain signals that enable rapid and effective motor adaptation.
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Affiliation(s)
- Dane Donegan
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Schwerzenbach 8603, Switzerland
| | - Christoph M Kanzler
- Rehabilitation Engineering Laboratory (RELab), Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Zürich 8008, Switzerland
| | - Julia Büscher
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Schwerzenbach 8603, Switzerland
| | - Paulius Viskaitis
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Schwerzenbach 8603, Switzerland
| | - Ed F Bracey
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Schwerzenbach 8603, Switzerland
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory (RELab), Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Zürich 8008, Switzerland
| | - Denis Burdakov
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Schwerzenbach 8603, Switzerland
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33
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Gokcen E, Jasper AI, Semedo JD, Zandvakili A, Kohn A, Machens CK, Yu BM. Disentangling the flow of signals between populations of neurons. NATURE COMPUTATIONAL SCIENCE 2022; 2:512-525. [PMID: 38177794 DOI: 10.1038/s43588-022-00282-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 06/21/2022] [Indexed: 01/06/2024]
Abstract
Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.
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Affiliation(s)
- Evren Gokcen
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Anna I Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - João D Semedo
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Amin Zandvakili
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, New York, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Christian K Machens
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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34
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McFadyen JR, Heider B, Karkhanis AN, Cloherty SL, Muñoz F, Siegel RM, Morris AP. Robust Coding of Eye Position in Posterior Parietal Cortex despite Context-Dependent Tuning. J Neurosci 2022; 42:4116-4130. [PMID: 35410881 PMCID: PMC9121829 DOI: 10.1523/jneurosci.0674-21.2022] [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: 03/30/2021] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/21/2022] Open
Abstract
Neurons in posterior parietal cortex (PPC) encode many aspects of the sensory world (e.g., scene structure), the posture of the body, and plans for action. For a downstream computation, however, only some of these dimensions are relevant; the rest are "nuisance variables" because their influence on neural activity changes with sensory and behavioral context, potentially corrupting the read-out of relevant information. Here we show that a key postural variable for vision (eye position) is represented robustly in male macaque PPC across a range of contexts, although the tuning of single neurons depended strongly on context. Contexts were defined by different stages of a visually guided reaching task, including (1) a visually sparse epoch, (2) a visually rich epoch, (3) a "go" epoch in which the reach was cued, and (4) during the reach itself. Eye position was constant within trials but varied across trials in a 3 × 3 grid spanning 24° × 24°. Using demixed principal component analysis of neural spike-counts, we found that the subspace of the population response encoding eye position is orthogonal to that encoding task context. Accordingly, a context-naive (fixed-parameter) decoder was nevertheless able to estimate eye position reliably across contexts. Errors were small given the sample size (∼1.78°) and would likely be even smaller with larger populations. Moreover, they were comparable to that of decoders that were optimized for each context. Our results suggest that population codes in PPC shield encoded signals from crosstalk to support robust sensorimotor transformations across contexts.SIGNIFICANCE STATEMENT Neurons in posterior parietal cortex (PPC) which are sensitive to gaze direction are thought to play a key role in spatial perception and behavior (e.g., reaching, navigation), and provide a potential substrate for brain-controlled prosthetics. Many, however, change their tuning under different sensory and behavioral contexts, raising the prospect that they provide unreliable representations of egocentric space. Here, we analyze the structure of encoding dimensions for gaze direction and context in PPC during different stages of a visually guided reaching task. We use demixed dimensionality reduction and decoding techniques to show that the coding of gaze direction in PPC is mostly invariant to context. This suggests that PPC can provide reliable spatial information across sensory and behavioral contexts.
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Affiliation(s)
- Jamie R McFadyen
- Neuroscience Program, Biomedicine Discovery Institute, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Barbara Heider
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Anushree N Karkhanis
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Shaun L Cloherty
- School of Engineering, RMIT University, Melbourne, VIC, 3001, Australia
| | - Fabian Muñoz
- Department of Neuroscience, Columbia University, New York, NY, 10027
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Ralph M Siegel
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Adam P Morris
- Neuroscience Program, Biomedicine Discovery Institute, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
- Monash Data Futures Institute, Monash University, Clayton, VIC, 3800, Australia
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35
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Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration. Nat Commun 2022; 13:2450. [PMID: 35508447 PMCID: PMC9068924 DOI: 10.1038/s41467-022-30069-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 04/08/2022] [Indexed: 11/09/2022] Open
Abstract
Animals can capitalize on invariance in the environment by learning and automating highly consistent actions; however, they must also remain flexible and adapt to environmental changes. It remains unclear how primary motor cortex (M1) can drive precise movements, yet also support behavioral exploration when faced with consistent errors. Using a reach-to-grasp task in rats, along with simultaneous electrophysiological monitoring in M1 and dorsolateral striatum (DLS), we find that behavioral exploration to overcome consistent task errors is closely associated with tandem increases in M1 and DLS neural variability; subsequently, consistent ensemble patterning returns with convergence to a new successful strategy. We also show that compared to reliably patterned intracranial microstimulation in M1, variable stimulation patterns result in significantly greater movement variability. Our results thus indicate that motor and striatal areas can flexibly transition between two modes, reliable neural pattern generation for automatic and precise movements versus variable neural patterning for behavioral exploration. It is not fully understood how behavioral flexibility is established in the context of automatic performance of a complex motor skill. Here the authors show that corticostriatal activity can flexibly transition between two modes during a reach to-grasp task in rats: reliable neural pattern generation for precise, automatic movements versus variable neural patterning for behavioral exploration.
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36
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Amini E, Yusof A, Riek S, Selvanayagam VS. Interaction of hand orientations during familiarization of a goal-directed aiming task. Hum Mov Sci 2022; 83:102955. [PMID: 35487099 DOI: 10.1016/j.humov.2022.102955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 12/21/2021] [Accepted: 04/18/2022] [Indexed: 11/17/2022]
Abstract
The purpose of the present study was to examine errors for an isometric goal-directed aiming task during familiarization at different hand orientation. Interaction between neutral and pronated hand orientations with and without directional feedback would provide insights into short-term adaptations and the nature of control. In this study, 30 healthy right-handed adults (age, 22.7 ± 3.1 years; weight, 69.4 ± 16.6 kg; height, 166.7 ± 7.9 cm) were randomly assigned to neutral or pronated hand orientation conditions. To assess familiarization, participants performed ten sets (16 targets/set) of goal-directed aiming task with continuous visual feedback towards targets symmetrically distributed about the origin. Following familiarization, participants then completed eight sets; four sets with and four sets without directional feedback, in an alternated order. For both hand orientations, directional errors were reduced in the first two sets (p < 0.05), suggesting only three sets were required for familiarization. Additionally, the learning rate was also similar for both hand orientations. Following familiarization, aiming errors without feedback were significantly higher than with feedback while no change between sets was observed, regardless of hand orientation. Aiming errors were reduced in the early phase with and without visual feedback, however, in the late phase, errors were corrected when visual feedback was provided. It suggests that hand orientation does not affect familiarization, and mechanisms similar to rapid learning may be involved. It is probable that learning is consolidated during familiarization along with feedforward input to maintain performance. In addition, proprioceptive feedback plays a role in reducing errors early, while the online visual feedback plays a role in reducing errors later, independent of hand orientation.
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Affiliation(s)
- Elaheh Amini
- Centre for Sport and Exercise Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Ashril Yusof
- Centre for Sport and Exercise Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Stephan Riek
- Graduate Research School, University of the Sunshine Coast, Locked Bag 4, Maroochydore DC 4558, Queensland, Australia; School of Human Movement and Nutrition Science, The University of Queensland, St Lucia 4072, Australia
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Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat Commun 2022; 13:1099. [PMID: 35232956 PMCID: PMC8888615 DOI: 10.1038/s41467-022-28552-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/19/2022] [Indexed: 12/19/2022] Open
Abstract
Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.
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38
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Stawsky A, Vashistha H, Salman H, Brenner N. Multiple timescales in bacterial growth homeostasis. iScience 2022; 25:103678. [PMID: 35118352 PMCID: PMC8792075 DOI: 10.1016/j.isci.2021.103678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/30/2021] [Accepted: 12/21/2021] [Indexed: 01/12/2023] Open
Abstract
In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are "sloppy" with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define "stiff" combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.
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Affiliation(s)
- Alejandro Stawsky
- Interdisciplinary Program in Applied Mathematics, Technion, Haifa, Israel
- Network Biology Research Laboratories, Technion, Haifa, Israel
| | - Harsh Vashistha
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Hanna Salman
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Naama Brenner
- Network Biology Research Laboratories, Technion, Haifa, Israel
- Department of Chemical Engineering, Technion, Haifa, Israel
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39
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Sun X, O'Shea DJ, Golub MD, Trautmann EM, Vyas S, Ryu SI, Shenoy KV. Cortical preparatory activity indexes learned motor memories. Nature 2022; 602:274-279. [PMID: 35082444 PMCID: PMC9851374 DOI: 10.1038/s41586-021-04329-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 12/09/2021] [Indexed: 01/21/2023]
Abstract
The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task1 in a curl force field that elicited new muscle forces for some, but not all, movement directions2,3. We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated4 existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field5,6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space7-9. The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire.
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Affiliation(s)
- Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Matthew D Golub
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Eric M Trautmann
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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40
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Huang Y, Yu Z. Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models. ENTROPY 2022; 24:e24020152. [PMID: 35205448 PMCID: PMC8871213 DOI: 10.3390/e24020152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/14/2022] [Accepted: 01/14/2022] [Indexed: 02/04/2023]
Abstract
Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural activities. Most existing LVMs are based on a point process and fail to model evolving relationships. In this work, we introduce a dynamic graph as the latent variable and develop a Variational Dynamic Graph Latent Variable Model (VDGLVM), a representation learning model based on the variational information bottleneck framework. VDGLVM utilizes a graph generative model and a graph neural network to capture dynamic communication between nodes that one has no access to from the observed data. The proposed computational model provides guaranteed behavior-decoding performance and improves LVMs by associating the inferred latent dynamics with probable DFC.
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41
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Thivierge JP, Pilzak A. Estimating null and potent modes of feedforward communication in a computational model of cortical activity. Sci Rep 2022; 12:742. [PMID: 35031628 PMCID: PMC8760251 DOI: 10.1038/s41598-021-04684-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 12/15/2021] [Indexed: 11/08/2022] Open
Abstract
Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.
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Affiliation(s)
- Jean-Philippe Thivierge
- School of Psychology, University of Ottawa, Ottawa, ON, Canada.
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada.
| | - Artem Pilzak
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
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42
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Going beyond primary motor cortex to improve brain–computer interfaces. Trends Neurosci 2022; 45:176-183. [DOI: 10.1016/j.tins.2021.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/01/2021] [Accepted: 12/19/2021] [Indexed: 01/08/2023]
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43
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Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning. J Neurosci 2021; 41:10120-10129. [PMID: 34732522 DOI: 10.1523/jneurosci.1397-21.2021] [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: 07/07/2021] [Revised: 09/29/2021] [Accepted: 10/11/2021] [Indexed: 11/21/2022] Open
Abstract
How does the brain integrate signals with different timescales to drive purposeful actions? Brain-machine interfaces (BMIs) offer a powerful tool to causally test how distributed neural networks achieve specific neural patterns. During neuroprosthetic learning, actuator movements are causally linked to primary motor cortex (M1) neurons, i.e., "direct" neurons that project to the decoder and whose firing is required to successfully perform the task. However, it is unknown how such direct M1 neurons interact with both "indirect" local (in M1 but not part of the decoder) and across area neural populations (e.g., in premotor cortex/M2), all of which are embedded in complex biological recurrent networks. Here, we trained male rats to perform a M1-BMI task and simultaneously recorded the activity of indirect neurons in both M2 and M1. We found that both M2 and M1 indirect neuron populations could be used to predict the activity of the direct neurons (i.e., "BMI-potent activity"). Interestingly, compared with M1 indirect activity, M2 neural activity was correlated with BMI-potent activity across a longer set of time lags, and the timescale of population activity patterns evolved more slowly. M2 units also predicted the activity of both M1 direct and indirect neural populations, suggesting that M2 population dynamics provide a continuous modulatory influence on M1 activity as a whole, rather than a moment-by-moment influence solely on neurons most relevant to a task. Together, our results indicate that longer timescale M2 activity provides modulatory influence over extended time lags on shorter-timescale control signals in M1.SIGNIFICANCE STATEMENT A central question in the study of motor control is whether primary motor cortex (M1) and premotor cortex (M2) interact through task-specific subpopulations of neurons, or whether tasks engage broader correlated networks. Brain-machine interfaces (BMIs) are powerful tools to study cross-area interactions. Here, we performed simultaneous recordings of M1 and M2 in a BMI task using a subpopulation of M1 neurons (direct neurons). We found that activity outside of direct neurons in M1 and M2 was predictive of M1-BMI task activity, and that M2 activity evolved at slower timescales than M1. These findings suggest that M2 provides a continuous modulatory influence on M1 as a whole, supporting a model of interactions through broad correlated networks rather than task-specific neural subpopulations.
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44
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Hennig JA, Oby ER, Losey DM, Batista AP, Yu BM, Chase SM. How learning unfolds in the brain: toward an optimization view. Neuron 2021; 109:3720-3735. [PMID: 34648749 PMCID: PMC8639641 DOI: 10.1016/j.neuron.2021.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/02/2021] [Indexed: 12/17/2022]
Abstract
How do changes in the brain lead to learning? To answer this question, consider an artificial neural network (ANN), where learning proceeds by optimizing a given objective or cost function. This "optimization framework" may provide new insights into how the brain learns, as many idiosyncratic features of neural activity can be recapitulated by an ANN trained to perform the same task. Nevertheless, there are key features of how neural population activity changes throughout learning that cannot be readily explained in terms of optimization and are not typically features of ANNs. Here we detail three of these features: (1) the inflexibility of neural variability throughout learning, (2) the use of multiple learning processes even during simple tasks, and (3) the presence of large task-nonspecific activity changes. We propose that understanding the role of these features in the brain will be key to describing biological learning using an optimization framework.
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Affiliation(s)
- Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Darby M Losey
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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45
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Altan E, Solla SA, Miller LE, Perreault EJ. Estimating the dimensionality of the manifold underlying multi-electrode neural recordings. PLoS Comput Biol 2021; 17:e1008591. [PMID: 34843461 PMCID: PMC8659648 DOI: 10.1371/journal.pcbi.1008591] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 12/09/2021] [Accepted: 11/11/2021] [Indexed: 01/07/2023] Open
Abstract
It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.
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Affiliation(s)
- Ege Altan
- Department of Neuroscience, Northwestern University, Chicago, Illinois, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Sara A. Solla
- Department of Neuroscience, Northwestern University, Chicago, Illinois, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois, United States of America
| | - Lee E. Miller
- Department of Neuroscience, Northwestern University, Chicago, Illinois, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, United States of America
- Shirley Ryan AbilityLab, Chicago, Illinois, United States of America
| | - Eric J. Perreault
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, United States of America
- Shirley Ryan AbilityLab, Chicago, Illinois, United States of America
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46
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Umakantha A, Morina R, Cowley BR, Snyder AC, Smith MA, Yu BM. Bridging neuronal correlations and dimensionality reduction. Neuron 2021; 109:2740-2754.e12. [PMID: 34293295 PMCID: PMC8505167 DOI: 10.1016/j.neuron.2021.06.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/05/2021] [Accepted: 06/25/2021] [Indexed: 01/01/2023]
Abstract
Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.
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Affiliation(s)
- Akash Umakantha
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rudina Morina
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Benjamin R Cowley
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Adam C Snyder
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester, NY 14642, USA
| | - Matthew A Smith
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Carnegie Mellon Neuroscience Institute, 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.
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47
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Abstract
Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA; .,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA
| | - Matthew D Golub
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA.,Google AI, Google Inc., Mountain View, California 94305, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA; .,Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA.,Department of Neurobiology, Bio-X Institute, Neurosciences Program, and Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
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48
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Eberle H, Hayashi Y, Kurazume R, Takei T, An Q. Modeling of hyper-adaptability: from motor coordination to rehabilitation. Adv Robot 2021. [DOI: 10.1080/01691864.2021.1943710] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Harry Eberle
- Department of Ortho and MSK Science Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, London, UK
| | - Yoshikatsu Hayashi
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, UK
| | - Ryo Kurazume
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Tomohiko Takei
- Graduate School of Medicine, Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan
| | - Qi An
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
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49
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Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire MP, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K, Shenoy KV. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nat Commun 2021; 12:3689. [PMID: 34140486 PMCID: PMC8211867 DOI: 10.1038/s41467-021-23884-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/19/2021] [Indexed: 02/05/2023] Open
Abstract
Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - James H Marshel
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ailey Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sam Vesuna
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Lucas Cofer
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Gergő Bohner
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Will Allen
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Isaac Kauvar
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sean Quirin
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Matthew P Whitmire
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | | | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Karl Deisseroth
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
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50
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Hennig JA, Oby ER, Golub MD, Bahureksa LA, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Chase SM, Yu BM. Learning is shaped by abrupt changes in neural engagement. Nat Neurosci 2021; 24:727-736. [PMID: 33782622 DOI: 10.1038/s41593-021-00822-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 02/22/2021] [Indexed: 01/30/2023]
Abstract
Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.
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Affiliation(s)
- Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA. .,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Lindsay A Bahureksa
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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