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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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2
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Marciniak Dg Agra K, Dg Agra P. F = ma. Is the macaque brain Newtonian? Cogn Neuropsychol 2023; 39:376-408. [PMID: 37045793 DOI: 10.1080/02643294.2023.2191843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.
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Affiliation(s)
- Karolina Marciniak Dg Agra
- The Rockefeller University, Laboratory of Neural Circuits, New York, NY, USA
- Center for Brain, Minds and Machines, Cambridge, MA, USA
| | - Pedro Dg Agra
- The Rockefeller University, Laboratory of Neural Circuits, New York, NY, USA
- Center for Brain, Minds and Machines, Cambridge, MA, USA
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3
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Inagaki HK, Chen S, Ridder MC, Sah P, Li N, Yang Z, Hasanbegovic H, Gao Z, Gerfen CR, Svoboda K. A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement. Cell 2022; 185:1065-1081.e23. [PMID: 35245431 PMCID: PMC8990337 DOI: 10.1016/j.cell.2022.02.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 11/15/2021] [Accepted: 02/03/2022] [Indexed: 01/06/2023]
Abstract
Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.
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Affiliation(s)
- Hidehiko K Inagaki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA.
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, UK
| | - Margreet C Ridder
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Pankaj Sah
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia; Joint Center for Neuroscience and Neural Engineering, and Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, China
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zidan Yang
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Hana Hasanbegovic
- Department of Neuroscience, Erasmus MC, Rotterdam, 3015GE, The Netherlands
| | - Zhenyu Gao
- Department of Neuroscience, Erasmus MC, Rotterdam, 3015GE, The Netherlands
| | | | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
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4
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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: 34] [Impact Index Per Article: 17.0] [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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5
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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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6
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Becker MI, Calame DJ, Wrobel J, Person AL. Online control of reach accuracy in mice. J Neurophysiol 2020; 124:1637-1655. [PMID: 32997569 PMCID: PMC7814908 DOI: 10.1152/jn.00324.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/02/2020] [Accepted: 09/20/2020] [Indexed: 01/06/2023] Open
Abstract
Reaching movements, as a basic yet complex motor behavior, are a foundational model system in neuroscience. In particular, there has been a significant recent expansion of investigation into the neural circuit mechanisms of reach behavior in mice. Nevertheless, quantification of mouse reach kinematics remains lacking, limiting comparison to the primate literature. In this study, we quantitatively demonstrate the homology of mouse reach kinematics to primate reach and also discover novel late-phase correlational structure that implies online control. Overall, our results highlight the decelerative phase of reach as important in driving successful outcome. Specifically, we develop and implement a novel statistical machine-learning algorithm to identify kinematic features associated with successful reaches and find that late-phase kinematics are most predictive of outcome, signifying online reach control as opposed to preplanning. Moreover, we identify and characterize late-phase kinematic adjustments that are yoked to midflight position and velocity of the limb, allowing for dynamic correction of initial variability, with head-fixed reaches being less dependent on position in comparison to freely behaving reaches. Furthermore, consecutive reaches exhibit positional error correction but not hot-handedness, implying opponent regulation of motor variability. Overall, our results establish foundational mouse reach kinematics in the context of neuroscientific investigation, characterizing mouse reach production as an active process that relies on dynamic online control mechanisms.NEW & NOTEWORTHY Mice use reaching movements to grasp and manipulate objects in their environment, similar to primates. To better establish mouse reach as a model for motor control, we implement several analytical frameworks, from basic kinematic relationships to statistical machine learning, to quantify mouse reach, finding many canonical features of primate reaches are conserved in mice, as well as evidence for midflight course corrections, expanding the utility of mouse reach paradigms for motor control studies.
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Affiliation(s)
- Matthew I Becker
- University of Colorado Neuroscience Graduate Program, Aurora, Colorado
- University of Colorado Medical Scientist Training Program, Aurora, Colorado
| | - Dylan J Calame
- University of Colorado Neuroscience Graduate Program, Aurora, Colorado
- University of Colorado Medical Scientist Training Program, Aurora, Colorado
| | - Julia Wrobel
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado
| | - Abigail L Person
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, Colorado
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7
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Vyas S, O'Shea DJ, Ryu SI, Shenoy KV. Causal Role of Motor Preparation during Error-Driven Learning. Neuron 2020; 106:329-339.e4. [PMID: 32053768 PMCID: PMC7185427 DOI: 10.1016/j.neuron.2020.01.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 11/12/2019] [Accepted: 01/16/2020] [Indexed: 11/28/2022]
Abstract
Current theories suggest that an error-driven learning process updates trial-by-trial to facilitate motor adaptation. How this process interacts with motor cortical preparatory activity-which current models suggest plays a critical role in movement initiation-remains unknown. Here, we evaluated the role of motor preparation during visuomotor adaptation. We found that preparation time was inversely correlated to variance of errors on current trials and mean error on subsequent trials. We also found causal evidence that intracortical microstimulation during motor preparation was sufficient to disrupt learning. Surprisingly, stimulation did not affect current trials, but instead disrupted the update computation of a learning process, thereby affecting subsequent trials. This is consistent with a Bayesian estimation framework where the motor system reduces its learning rate by virtue of lowering error sensitivity when faced with uncertainty. This interaction between motor preparation and the error-driven learning system may facilitate new probes into mechanisms underlying trial-by-trial adaptation.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Daniel J O'Shea
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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8
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Baseline Motor Cortex Activity Contains an Internal Model Representation. J Neurosci 2019; 37:6389-6390. [PMID: 28679797 DOI: 10.1523/jneurosci.1016-17.2017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 05/18/2017] [Accepted: 05/25/2017] [Indexed: 11/21/2022] Open
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9
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Abstract
Coordinated movement depends on constant interaction between neural circuits that produce motor output and those that report sensory consequences. Fundamental to this process are mechanisms for controlling the influence that sensory signals have on motor pathways - for example, reducing feedback gains when they are disruptive and increasing gains when advantageous. Sensory gain control comes in many forms and serves diverse purposes - in some cases sensory input is attenuated to maintain movement stability and filter out irrelevant or self-generated signals, or enhanced to facilitate salient signals for improved movement execution and adaptation. The ubiquitous presence of sensory gain control across species at multiple levels of the nervous system reflects the importance of tuning the impact that feedback information has on behavioral output.
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10
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Quick KM, Mischel JL, Loughlin PJ, Batista AP. The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates. J Neurophysiol 2018; 120:2164-2181. [PMID: 29947593 DOI: 10.1152/jn.00300.2017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Everyday behaviors require that we interact with the environment, using sensory information in an ongoing manner to guide our actions. Yet, by design, many of the tasks used in primate neurophysiology laboratories can be performed with limited sensory guidance. As a consequence, our knowledge about the neural mechanisms of motor control is largely limited to the feedforward aspects of the motor command. To study the feedback aspects of volitional motor control, we adapted the critical stability task (CST) from the human performance literature (Jex H, McDonnell J, Phatak A. IEEE Trans Hum Factors Electron 7: 138-145, 1966). In the CST, our monkey subjects interact with an inherently unstable (i.e., divergent) virtual system and must generate sensory-guided actions to stabilize it about an equilibrium point. The difficulty of the CST is determined by a single parameter, which allows us to quantitatively establish the limits of performance in the task for different sensory feedback conditions. Two monkeys learned to perform the CST with visual or vibrotactile feedback. Performance was better under visual feedback, as expected, but both monkeys were able to utilize vibrotactile feedback alone to successfully perform the CST. We also observed changes in behavioral strategy as the task became more challenging. The CST will have value for basic science investigations of the neural basis of sensory-motor integration during ongoing actions, and it may also provide value for the design and testing of bidirectional brain computer interface systems. NEW & NOTEWORTHY Currently, most behavioral tasks used in motor neurophysiology studies require primates to make short-duration, stereotyped movements that do not necessitate sensory feedback. To improve our understanding of sensorimotor integration, and to engineer meaningful artificial sensory feedback systems for brain-computer interfaces, it is crucial to have a task that requires sensory feedback for good control. The critical stability task demands that sensory information be used to guide long-duration movements.
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Affiliation(s)
- Kristin M Quick
- Department of Bioengineering, University of Pittsburgh , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition , Pittsburgh, Pennsylvania
| | - Jessica L Mischel
- Department of Bioengineering, University of Pittsburgh , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition , Pittsburgh, Pennsylvania
| | - Patrick J Loughlin
- Department of Bioengineering, University of Pittsburgh , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition , Pittsburgh, Pennsylvania
| | - Aaron P Batista
- Department of Bioengineering, University of Pittsburgh , Pittsburgh, Pennsylvania.,Center for the Neural Basis of Cognition , Pittsburgh, Pennsylvania
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11
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Pandarinath C, Ames KC, Russo AA, Farshchian A, Miller LE, Dyer EL, Kao JC. Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces. J Neurosci 2018; 38:9390-9401. [PMID: 30381431 PMCID: PMC6209846 DOI: 10.1523/jneurosci.1669-18.2018] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 09/24/2018] [Accepted: 09/25/2018] [Indexed: 01/07/2023] Open
Abstract
In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.
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Affiliation(s)
- Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322,
- Department of Neurosurgery, Emory University, Atlanta, Georgia 30322
| | - K Cora Ames
- Department of Neuroscience
- Center for Theoretical Neuroscience
- Grossman Center for the Statistics of Mind
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Abigail A Russo
- Department of Neuroscience
- Grossman Center for the Statistics of Mind
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Ali Farshchian
- Department of Physiology, Northwestern University, Chicago, Illinois 60611
| | - Lee E Miller
- Department of Physiology, Northwestern University, Chicago, Illinois 60611
| | - Eva L Dyer
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, and
- Neurosciences Program, University of California, Los Angeles, California 90095
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12
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Perich MG, Gallego JA, Miller LE. A Neural Population Mechanism for Rapid Learning. Neuron 2018; 100:964-976.e7. [PMID: 30344047 DOI: 10.1016/j.neuron.2018.09.030] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/16/2018] [Accepted: 09/21/2018] [Indexed: 12/18/2022]
Abstract
Long-term learning of language, mathematics, and motor skills likely requires cortical plasticity, but behavior often requires much faster changes, sometimes even after single errors. Here, we propose one neural mechanism to rapidly develop new motor output without altering the functional connectivity within or between cortical areas. We tested cortico-cortical models relating the activity of hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices throughout adaptation to reaching movement perturbations. We found a signature of learning in the "output-null" subspace of PMd with respect to M1 reflecting the ability of premotor cortex to alter preparatory activity without directly influencing M1. The output-null subspace planning activity evolved with adaptation, yet the "output-potent" mapping that captures information sent to M1 was preserved. Our results illustrate a population-level cortical mechanism to progressively adjust the output from one brain area to its downstream structures that could be exploited for rapid behavioral adaptation.
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Affiliation(s)
- Matthew G Perich
- Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611, USA
| | - Juan A Gallego
- Department of Physiology, Northwestern University, Chicago, IL 60611, USA; Neural and Cognitive Engineering Group, Centre for Automation and Robotics, CSIC-UPM, 28500 Arganda del Rey, Madrid, Spain
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611, USA; Department of Physiology, Northwestern University, Chicago, IL 60611, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA.
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13
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Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat Neurosci 2018; 21:736-743. [PMID: 29662213 PMCID: PMC6054128 DOI: 10.1038/s41593-018-0136-y] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 03/07/2018] [Indexed: 12/15/2022]
Abstract
The primary output cells of the cerebellar cortex, Purkinje cells, make kinematic predictions about ongoing movements via high-frequency simple spikes, but receive sensory error information about that movement via low-frequency complex spikes (CS). How is the vector space of sensory errors encoded by this low-frequency signal? Here we measured Purkinje cell activity in the oculomotor vermis of animals during saccades, then followed the chain of events from experience of visual error, generation of CS, modulation of simple spikes, and ultimately change in motor output. We found that while error direction affected the probability of CS, error magnitude altered its temporal distribution. Production of CS changed the simple spikes on the next trial, but regardless of the actual visual error, this change biased the movement only along a vector that was parallel to the Purkinje cell's preferred error. From these results, we inferred the anatomy of a sensory-to-motor adaptive controller that transformed visual error vectors into motor-corrections.
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14
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Hauser CK, Zhu D, Stanford TR, Salinas E. Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets. eLife 2018; 7:33456. [PMID: 29652247 PMCID: PMC5947991 DOI: 10.7554/elife.33456] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 04/12/2018] [Indexed: 01/26/2023] Open
Abstract
In studies of voluntary movement, a most elemental quantity is the reaction time (RT) between the onset of a visual stimulus and a saccade toward it. However, this RT demonstrates extremely high variability which, in spite of extensive research, remains unexplained. It is well established that, when a visual target appears, oculomotor activity gradually builds up until a critical level is reached, at which point a saccade is triggered. Here, based on computational work and single-neuron recordings from monkey frontal eye field (FEF), we show that this rise-to-threshold process starts from a dynamic initial state that already contains other incipient, internally driven motor plans, which compete with the target-driven activity to varying degrees. The ensuing conflict resolution process, which manifests in subtle covariations between baseline activity, build-up rate, and threshold, consists of fundamentally deterministic interactions, and explains the observed RT distributions while invoking only a small amount of intrinsic randomness. As we examine the space around us our eyes move in short steps, looking toward a new location about four times a second. Neurons in a region of the brain called the frontal eye field help initiate these eye movements, which are known as saccades. Each neuron contributes to a saccade with a specific direction and size. Before a saccade, the relevant neurons in the frontal eye field steadily increase their activity. When this activity reaches a critical threshold, the visual system issues a command to move the eyes in the appropriate direction. So a saccade that moves the eyes to the right requires a specific group of neurons to be strongly activated – but, at the same time, the neurons responsible for movement to the left need to be less active. Imagine that you have to move your eyes as quickly as possible to look at a spot of light that appears on a screen. Some of the time your eyes will start to move about 100 milliseconds after the light appears. But on other attempts, your eyes will not start moving until 300 milliseconds after the light came on. What causes this variability? To find out, Hauser et al. recorded from neurons in monkeys trained to perform such a task. When the spot of light appeared many different neurons were active, suggesting there is conflict between the plan that would move the eyes toward the target and plans to look at other locations. That is, when the target appears, the monkey is already thinking of looking somewhere. The time required to resolve this conflict depends on how far apart the target and the competing locations are from one another, and on how much the competing neurons have increased their activity before the target appears. Similar mechanisms are likely to operate when we sit at the dinner table and look for the salt shaker, for example, and so the results presented by Hauser et al. will help us to understand how we direct our attention to different points in space. Understanding how these processes work in more detail will help us to discern what happens when they go wrong, as occurs in attention deficit disorders like ADHD.
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Affiliation(s)
- Christopher K Hauser
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Dantong Zhu
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Terrence R Stanford
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Emilio Salinas
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
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Vyas S, Even-Chen N, Stavisky SD, Ryu SI, Nuyujukian P, Shenoy KV. Neural Population Dynamics Underlying Motor Learning Transfer. Neuron 2018; 97:1177-1186.e3. [PMID: 29456026 DOI: 10.1016/j.neuron.2018.01.040] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 11/21/2017] [Accepted: 01/20/2018] [Indexed: 12/22/2022]
Abstract
Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Nir Even-Chen
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA
| | - Sergey D Stavisky
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Paul Nuyujukian
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Thompson GJ, Sanganahalli BG, Baker KL, Herman P, Shepherd GM, Verhagen JV, Hyder F. Spontaneous activity forms a foundation for odor-evoked activation maps in the rat olfactory bulb. Neuroimage 2018; 172:586-596. [PMID: 29374582 DOI: 10.1016/j.neuroimage.2018.01.051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/16/2018] [Accepted: 01/20/2018] [Indexed: 12/12/2022] Open
Abstract
Fluctuations in spontaneous activity have been observed by many neuroimaging techniques, but because these resting-state changes are not evoked by stimuli, it is difficult to determine how they relate to task-evoked activations. We conducted multi-modal neuroimaging scans of the rat olfactory bulb, both with and without odor, to examine interaction between spontaneous and evoked activities. Independent component analysis of spontaneous fluctuations revealed resting-state networks, and odor-evoked changes revealed activation maps. We constructed simulated activation maps using resting-state networks that were highly correlated to evoked activation maps. Simulated activation maps derived by intrinsic optical signal (IOS), which covers the dorsal portion of the glomerular sheet, significantly differentiated one odor's evoked activation map from the other two. To test the hypothesis that spontaneous activity of the entire glomerular sheet is relevant for representing odor-evoked activations, we used functional magnetic resonance imaging (fMRI) to map the entire glomerular sheet. In contrast to the IOS results, the fMRI-derived simulated activation maps significantly differentiated all three odors' evoked activation maps. Importantly, no evoked activation maps could be significantly differentiated using simulated activation maps produced using phase-randomized resting-state networks. Given that some highly organized resting-state networks did not correlate with any odors' evoked activation maps, we posit that these resting-state networks may characterize evoked activation maps associated with odors not studied. These results emphasize that fluctuations in spontaneous activity form a foundation for active processing, signifying the relevance of resting-state mapping to functional neuroimaging.
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Affiliation(s)
- Garth J Thompson
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Basavaraju G Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA
| | - Keeley L Baker
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA
| | | | - Justus V Verhagen
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
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17
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Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Augmenting intracortical brain-machine interface with neurally driven error detectors. J Neural Eng 2017; 14:066007. [PMID: 29130452 PMCID: PMC5742283 DOI: 10.1088/1741-2552/aa8dc1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. APPROACH We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. MAIN RESULTS We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). SIGNIFICANCE Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
| | - Sergey D. Stavisky
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery, Stanford University, Stanford, CA 94305 USA
| | - Jonathan C. Kao
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095 USA
| | - Stephen I. Ryu
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery at Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Krishna V. Shenoy
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA 94305
- The Bio-X Program, Stanford University, Stanford, CA 94305
- The Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305
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