1
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Gorko B, Siwanowicz I, Close K, Christoforou C, Hibbard KL, Kabra M, Lee A, Park JY, Li SY, Chen AB, Namiki S, Chen C, Tuthill JC, Bock DD, Rouault H, Branson K, Ihrke G, Huston SJ. Motor neurons generate pose-targeted movements via proprioceptive sculpting. Nature 2024; 628:596-603. [PMID: 38509371 DOI: 10.1038/s41586-024-07222-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/22/2024] [Indexed: 03/22/2024]
Abstract
Motor neurons are the final common pathway1 through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2 suppress a single class of proprioceptive neuron3 that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.
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Affiliation(s)
- Benjamin Gorko
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Igor Siwanowicz
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kari Close
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Mayank Kabra
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Allen Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jin-Yong Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Si Ying Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Alex B Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA
| | - Shigehiro Namiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Chenghao Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Neurological Sciences, University of Vermont, Burlington, VT, USA
| | - Hervé Rouault
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Turing Centre for Living systems, Aix-Marseille University, Université de Toulon, CNRS, CPT (UMR 7332), Marseille, France
| | - Kristin Branson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gudrun Ihrke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephen J Huston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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2
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Gebehart C, Büschges A. The processing of proprioceptive signals in distributed networks: insights from insect motor control. J Exp Biol 2024; 227:jeb246182. [PMID: 38180228 DOI: 10.1242/jeb.246182] [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: 01/06/2024]
Abstract
The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.
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Affiliation(s)
- Corinna Gebehart
- Champalimaud Foundation, Champalimaud Research, 1400-038 Lisbon, Portugal
| | - Ansgar Büschges
- Department of Animal Physiology, Institute of Zoology, Biocenter Cologne, University of Cologne, Zülpicher Strasse 47b, 50674 Cologne, Germany
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3
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Simha SN, Ting LH. Intrafusal cross-bridge dynamics shape history-dependent muscle spindle responses to stretch. Exp Physiol 2024; 109:112-124. [PMID: 37428622 PMCID: PMC10776813 DOI: 10.1113/ep090767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 06/23/2023] [Indexed: 07/12/2023]
Abstract
Computational models can be critical to linking complex properties of muscle spindle organs to the sensory information that they encode during behaviours such as postural sway and locomotion where few muscle spindle recordings exist. Here, we augment a biophysical muscle spindle model to predict the muscle spindle sensory signal. Muscle spindles comprise several intrafusal muscle fibres with varied myosin expression and are innervated by sensory neurons that fire during muscle stretch. We demonstrate how cross-bridge dynamics from thick and thin filament interactions affect the sensory receptor potential at the spike initiating region. Equivalent to the Ia afferent's instantaneous firing rate, the receptor potential is modelled as a linear sum of the force and rate change of force (yank) of a dynamic bag1 fibre and the force of a static bag2/chain fibre. We show the importance of inter-filament interactions in (i) generating large changes in force at stretch onset that drive initial bursts and (ii) faster recovery of bag fibre force and receptor potential following a shortening. We show how myosin attachment and detachment rates qualitatively alter the receptor potential. Finally, we show the effect of faster recovery of receptor potential on cyclic stretch-shorten cycles. Specifically, the model predicts history-dependence in muscle spindle receptor potentials as a function of inter-stretch interval (ISI), pre-stretch amplitude and the amplitude of sinusoidal stretches. This model provides a computational platform for predicting muscle spindle response in behaviourally relevant stretches and can link myosin expression seen in healthy and diseased intrafusal muscle fibres to muscle spindle function.
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Affiliation(s)
- Surabhi N. Simha
- Wallace H. Coulter Department of Biomedical EngineeringEmory University and The Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Lena H. Ting
- Wallace H. Coulter Department of Biomedical EngineeringEmory University and The Georgia Institute of TechnologyAtlantaGeorgiaUSA
- Department of Rehabilitation Medicine, Division of Physical TherapyEmory UniversityAtlantaGeorgiaUSA
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4
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Chung B, Zia M, Thomas KA, Michaels JA, Jacob A, Pack A, Williams MJ, Nagapudi K, Teng LH, Arrambide E, Ouellette L, Oey N, Gibbs R, Anschutz P, Lu J, Wu Y, Kashefi M, Oya T, Kersten R, Mosberger AC, O'Connell S, Wang R, Marques H, Mendes AR, Lenschow C, Kondakath G, Kim JJ, Olson W, Quinn KN, Perkins P, Gatto G, Thanawalla A, Coltman S, Kim T, Smith T, Binder-Markey B, Zaback M, Thompson CK, Giszter S, Person A, Goulding M, Azim E, Thakor N, O'Connor D, Trimmer B, Lima SQ, Carey MR, Pandarinath C, Costa RM, Pruszynski JA, Bakir M, Sober SJ. Myomatrix arrays for high-definition muscle recording. eLife 2023; 12:RP88551. [PMID: 38113081 PMCID: PMC10730117 DOI: 10.7554/elife.88551] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023] Open
Abstract
Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.
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Affiliation(s)
- Bryce Chung
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Muneeb Zia
- School of Electrical and Computer Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Kyle A Thomas
- Graduate Program in Biomedical Engineering at Emory University and Georgia TechAtlantaUnited States
| | | | - Amanda Jacob
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Andrea Pack
- Neuroscience Graduate Program, Emory UniversityAtlantaUnited States
| | - Matthew J Williams
- Graduate Program in Biomedical Engineering at Emory University and Georgia TechAtlantaUnited States
| | | | - Lay Heng Teng
- Department of Biology, Emory UniversityAtlantaUnited States
| | | | | | - Nicole Oey
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Rhuna Gibbs
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Philip Anschutz
- Graduate Program in BioEngineering, Georgia TechAtlantaUnited States
| | - Jiaao Lu
- Graduate Program in Electrical and Computer Engineering, Georgia TechAtlantaUnited States
| | - Yu Wu
- School of Electrical and Computer Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Mehrdad Kashefi
- Department of Physiology and Pharmacology, Western UniversityLondonCanada
| | - Tomomichi Oya
- Department of Physiology and Pharmacology, Western UniversityLondonCanada
| | - Rhonda Kersten
- Department of Physiology and Pharmacology, Western UniversityLondonCanada
| | - Alice C Mosberger
- Zuckerman Mind Brain Behavior Institute at Columbia UniversityNew YorkUnited States
| | - Sean O'Connell
- Graduate Program in Biomedical Engineering at Emory University and Georgia TechAtlantaUnited States
| | - Runming Wang
- Department of Biomedical Engineering at Emory University and Georgia TechAtlantaUnited States
| | - Hugo Marques
- Champalimaud Neuroscience Programme, Champalimaud FoundationLisbonPortugal
| | - Ana Rita Mendes
- Champalimaud Neuroscience Programme, Champalimaud FoundationLisbonPortugal
| | - Constanze Lenschow
- Champalimaud Neuroscience Programme, Champalimaud FoundationLisbonPortugal
| | | | - Jeong Jun Kim
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
| | - William Olson
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Kiara N Quinn
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Pierce Perkins
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Graziana Gatto
- Salk Institute for Biological StudiesLa JollaUnited States
| | | | - Susan Coltman
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Taegyo Kim
- Department of Neurobiology & Anatomy, Drexel University, College of MedicinePhiladelphiaUnited States
| | - Trevor Smith
- Department of Neurobiology & Anatomy, Drexel University, College of MedicinePhiladelphiaUnited States
| | - Ben Binder-Markey
- Department of Physical Therapy and Rehabilitation Sciences, Drexel University College of Nursing and Health ProfessionsPhiladelphiaUnited States
| | - Martin Zaback
- Department of Health and Rehabilitation Sciences, Temple UniversityPhiladelphiaUnited States
| | - Christopher K Thompson
- Department of Health and Rehabilitation Sciences, Temple UniversityPhiladelphiaUnited States
| | - Simon Giszter
- Department of Neurobiology & Anatomy, Drexel University, College of MedicinePhiladelphiaUnited States
| | - Abigail Person
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical CampusAuroraUnited States
- Allen InstituteSeattleUnited States
| | | | - Eiman Azim
- Salk Institute for Biological StudiesLa JollaUnited States
| | - Nitish Thakor
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Daniel O'Connor
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Barry Trimmer
- Department of Biology, Tufts UniversityMedfordUnited States
| | - Susana Q Lima
- Champalimaud Neuroscience Programme, Champalimaud FoundationLisbonPortugal
| | - Megan R Carey
- Champalimaud Neuroscience Programme, Champalimaud FoundationLisbonPortugal
| | - Chethan Pandarinath
- Department of Biomedical Engineering at Emory University and Georgia TechAtlantaUnited States
| | - Rui M Costa
- Zuckerman Mind Brain Behavior Institute at Columbia UniversityNew YorkUnited States
| | | | - Muhannad Bakir
- School of Electrical and Computer Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Samuel J Sober
- Department of Biology, Emory UniversityAtlantaUnited States
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5
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Wilson E. Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs. Neural Comput 2023; 35:1938-1969. [PMID: 37844325 DOI: 10.1162/neco_a_01617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 06/05/2023] [Indexed: 10/18/2023]
Abstract
Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.
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Affiliation(s)
- Emma Wilson
- School of Computing and Communications, Lancaster University, Lancaster LA1 4WA, U.K.
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6
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Pasini FW, Busch AN, Mináč J, Padmanabhan K, Muller L. Algebraic approach to spike-time neural codes in the hippocampus. Phys Rev E 2023; 108:054404. [PMID: 38115483 DOI: 10.1103/physreve.108.054404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/14/2023] [Indexed: 12/21/2023]
Abstract
Although temporal coding through spike-time patterns has long been of interest in neuroscience, the specific structures that could be useful for spike-time codes remain highly unclear. Here, we introduce an analytical approach, using techniques from discrete mathematics, to study spike-time codes. As an initial example, we focus on the phenomenon of "phase precession" in the rodent hippocampus. During navigation and learning on a physical track, specific cells in a rodent's brain form a highly structured pattern relative to the oscillation of population activity in this region. Studies of phase precession largely focus on its role in precisely ordering spike times for synaptic plasticity, as the role of phase precession in memory formation is well established. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for a function mapping points in physical space to complex-valued spikes by representing individual spike times as complex numbers. The properties of this function make explicit a specific relationship between past and future in spike patterns of the hippocampus. Importantly, this mathematical approach generalizes beyond the specific phenomenon studied here, providing a technique to study the neural codes within precise spike-time sequences found during sensory coding and motor behavior. We then introduce a spike-based decoding algorithm, based on this function, that successfully decodes a simulated animal's trajectory using only the animal's initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average firing rate. These results illustrate the utility of a discrete approach, based on the structure and symmetries in spike patterns across finite sets of cells, to provide insight into the structure and function of neural systems.
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Affiliation(s)
- Federico W Pasini
- Department of Mathematics, Western University London, Ontario, Canada N6A 5B7
- Western Academy for Advanced Research, Western University, London, Ontario, Canada N6A 5B7
- Western Institute for Neuroscience, Western University, London, Ontario, Canada N6A 5B7
| | - Alexandra N Busch
- Department of Mathematics, Western University London, Ontario, Canada N6A 5B7
- Western Academy for Advanced Research, Western University, London, Ontario, Canada N6A 5B7
- Western Institute for Neuroscience, Western University, London, Ontario, Canada N6A 5B7
| | - Ján Mináč
- Department of Mathematics, Western University London, Ontario, Canada N6A 5B7
- Western Academy for Advanced Research, Western University, London, Ontario, Canada N6A 5B7
- Western Institute for Neuroscience, Western University, London, Ontario, Canada N6A 5B7
| | - Krishnan Padmanabhan
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York 14642, USA
| | - Lyle Muller
- Department of Mathematics, Western University London, Ontario, Canada N6A 5B7
- Western Academy for Advanced Research, Western University, London, Ontario, Canada N6A 5B7
- Western Institute for Neuroscience, Western University, London, Ontario, Canada N6A 5B7
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7
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Chung B, Zia M, Thomas KA, Michaels JA, Jacob A, Pack A, Williams MJ, Nagapudi K, Teng LH, Arrambide E, Ouellette L, Oey N, Gibbs R, Anschutz P, Lu J, Wu Y, Kashefi M, Oya T, Kersten R, Mosberger AC, O'Connell S, Wang R, Marques H, Mendes AR, Lenschow C, Kondakath G, Kim JJ, Olson W, Quinn KN, Perkins P, Gatto G, Thanawalla A, Coltman S, Kim T, Smith T, Binder-Markey B, Zaback M, Thompson CK, Giszter S, Person A, Goulding M, Azim E, Thakor N, O'Connor D, Trimmer B, Lima SQ, Carey MR, Pandarinath C, Costa RM, Pruszynski JA, Bakir M, Sober SJ. Myomatrix arrays for high-definition muscle recording. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529200. [PMID: 36865176 PMCID: PMC9980060 DOI: 10.1101/2023.02.21.529200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.
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Affiliation(s)
- Bryce Chung
- Department of Biology, Emory University (Atlanta, GA, USA)
| | - Muneeb Zia
- School of Electrical and Computer Engineering, Georgia Institute of Technology (Atlanta, GA, USA)
| | - Kyle A Thomas
- Graduate Program in Biomedical Engineering at Emory University and Georgia Tech (Atlanta, GA, USA)
| | - Jonathan A Michaels
- Department of Physiology and Pharmacology, Western University (London, ON, Canada)
| | - Amanda Jacob
- Department of Biology, Emory University (Atlanta, GA, USA)
| | - Andrea Pack
- Neuroscience Graduate Program, Emory University (Atlanta, GA, USA)
| | - Matthew J Williams
- Graduate Program in Biomedical Engineering at Emory University and Georgia Tech (Atlanta, GA, USA)
| | | | - Lay Heng Teng
- Department of Biology, Emory University (Atlanta, GA, USA)
| | | | | | - Nicole Oey
- Department of Biology, Emory University (Atlanta, GA, USA)
| | - Rhuna Gibbs
- Department of Biology, Emory University (Atlanta, GA, USA)
| | - Philip Anschutz
- Graduate Program in BioEngineering, Georgia Tech (Atlanta, GA, USA)
| | - Jiaao Lu
- Graduate Program in Electrical and Computer Engineering, Georgia Tech (Atlanta, GA, USA)
| | - Yu Wu
- School of Electrical and Computer Engineering, Georgia Institute of Technology (Atlanta, GA, USA)
| | - Mehrdad Kashefi
- Department of Physiology and Pharmacology, Western University (London, ON, Canada)
| | - Tomomichi Oya
- Department of Physiology and Pharmacology, Western University (London, ON, Canada)
| | - Rhonda Kersten
- Department of Physiology and Pharmacology, Western University (London, ON, Canada)
| | - Alice C Mosberger
- Zuckerman Mind Brain Behavior Institute at Columbia University (New York, NY, USA)
| | - Sean O'Connell
- Graduate Program in Biomedical Engineering at Emory University and Georgia Tech (Atlanta, GA, USA)
| | - Runming Wang
- Department of Biomedical Engineering at Emory University and Georgia Tech (Atlanta, GA, USA)
| | - Hugo Marques
- Champalimaud Neuroscience Programme, Champalimaud Foundation (Lisbon, Portugal)
| | - Ana Rita Mendes
- Champalimaud Neuroscience Programme, Champalimaud Foundation (Lisbon, Portugal)
| | - Constanze Lenschow
- Champalimaud Neuroscience Programme, Champalimaud Foundation (Lisbon, Portugal)
- current address: Institute of Biology, Otto-von-Guericke University, (Magdeburg, Germany)
| | | | - Jeong Jun Kim
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - William Olson
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - Kiara N Quinn
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - Pierce Perkins
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - Graziana Gatto
- Salk Institute for Biological Studies (La Jolla, CA, USA)
- current address: Department of Neurology, University Hospital of Cologne (Cologne, Germany)
| | | | - Susan Coltman
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus (Aurora, CO, USA)
| | - Taegyo Kim
- Department of Neurobiology & Anatomy, Drexel University, College of Medicine (Philadelphia, PA, USA)
| | - Trevor Smith
- Department of Neurobiology & Anatomy, Drexel University, College of Medicine (Philadelphia, PA, USA)
| | - Ben Binder-Markey
- Department of Physical Therapy and Rehabilitation Sciences, Drexel University College of Nursing and Health Professions (Philadelphia, PA)
| | - Martin Zaback
- Department of Health and Rehabilitation Sciences, Temple University (Philadelphia, PA, USA)
| | - Christopher K Thompson
- Department of Health and Rehabilitation Sciences, Temple University (Philadelphia, PA, USA)
| | - Simon Giszter
- Department of Neurobiology & Anatomy, Drexel University, College of Medicine (Philadelphia, PA, USA)
| | - Abigail Person
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus (Aurora, CO, USA)
| | | | - Eiman Azim
- Salk Institute for Biological Studies (La Jolla, CA, USA)
| | - Nitish Thakor
- Departments of Biomedical Engineering and Neurology, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - Daniel O'Connor
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine (Baltimore, MD, USA)
| | - Barry Trimmer
- Department of Biology, Tufts University (Medford, MA, USA)
| | - Susana Q Lima
- Champalimaud Neuroscience Programme, Champalimaud Foundation (Lisbon, Portugal)
| | - Megan R Carey
- Champalimaud Neuroscience Programme, Champalimaud Foundation (Lisbon, Portugal)
| | - Chethan Pandarinath
- Department of Biomedical Engineering at Emory University and Georgia Tech (Atlanta, GA, USA)
| | - Rui M Costa
- Zuckerman Mind Brain Behavior Institute at Columbia University (New York, NY, USA)
- Allen Institute (Seattle, WA, USA)
| | - J Andrew Pruszynski
- Department of Physiology and Pharmacology, Western University (London, ON, Canada)
| | - Muhannad Bakir
- School of Electrical and Computer Engineering, Georgia Institute of Technology (Atlanta, GA, USA)
| | - Samuel J Sober
- Department of Biology, Emory University (Atlanta, GA, USA)
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8
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Herzfeld DJ, Joshua M, Lisberger SG. Rate versus synchrony codes for cerebellar control of motor behavior. Neuron 2023; 111:2448-2460.e6. [PMID: 37536289 PMCID: PMC10424531 DOI: 10.1016/j.neuron.2023.07.002] [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: 02/23/2023] [Revised: 05/24/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the code for information transmission from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellum of rhesus macaques reveal how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and insufficient to impact the firing of downstream vestibular nucleus neurons. Analysis of previous metrics that purported to reveal Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate rather than a synchrony code to drive the activity of downstream neurons and thus control motor behavior.
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Affiliation(s)
- David J Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Mati Joshua
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Stephen G Lisberger
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA
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9
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Putney J, Niebur T, Wood L, Conn R, Sponberg S. An information theoretic method to resolve millisecond-scale spike timing precision in a comprehensive motor program. PLoS Comput Biol 2023; 19:e1011170. [PMID: 37307288 PMCID: PMC10289674 DOI: 10.1371/journal.pcbi.1011170] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/23/2023] [Accepted: 05/10/2023] [Indexed: 06/14/2023] Open
Abstract
Sensory inputs in nervous systems are often encoded at the millisecond scale in a precise spike timing code. There is now growing evidence in behaviors ranging from slow breathing to rapid flight for the prevalence of precise timing encoding in motor systems. Despite this, we largely do not know at what scale timing matters in these circuits due to the difficulty of recording a complete set of spike-resolved motor signals and assessing spike timing precision for encoding continuous motor signals. We also do not know if the precision scale varies depending on the functional role of different motor units. We introduce a method to estimate spike timing precision in motor circuits using continuous MI estimation at increasing levels of added uniform noise. This method can assess spike timing precision at fine scales for encoding rich motor output variation. We demonstrate the advantages of this approach compared to a previously established discrete information theoretic method of assessing spike timing precision. We use this method to analyze the precision in a nearly complete, spike resolved recording of the 10 primary wing muscles control flight in an agile hawk moth, Manduca sexta. Tethered moths visually tracked a robotic flower producing a range of turning (yaw) torques. We know that all 10 muscles in this motor program encode the majority of information about yaw torque in spike timings, but we do not know whether individual muscles encode motor information at different levels of precision. We demonstrate that the scale of temporal precision in all motor units in this insect flight circuit is at the sub-millisecond or millisecond-scale, with variation in precision scale present between muscle types. This method can be applied broadly to estimate spike timing precision in sensory and motor circuits in both invertebrates and vertebrates.
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Affiliation(s)
- Joy Putney
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Tobias Niebur
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Leo Wood
- Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Rachel Conn
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Neuroscience Program, Emory University, Atlanta, Georgia, United States of America
| | - Simon Sponberg
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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10
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Herzfeld DJ, Joshua M, Lisberger SG. Rate versus synchrony codes for cerebellar control of motor behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.529019. [PMID: 36824885 PMCID: PMC9949136 DOI: 10.1101/2023.02.17.529019] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
UNLABELLED Control of movement requires the coordination of multiple brain areas, each containing populations of neurons that receive inputs, process these inputs via recurrent dynamics, and then relay the processed information to downstream populations. Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the nature of the code for transmission of signals to downstream areas from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellar flocculus of rhesus macaques revealed how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and likely insufficient to impact the firing of downstream neurons in the vestibular nucleus. Further, analysis of previous metrics for assaying Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate code rather than synchrony code to drive activity of downstream neurons and thus control motor behavior. IMPACT STATEMENT Information transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.
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Affiliation(s)
- David J. Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Mati Joshua
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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11
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Menelaou E, Kishore S, McLean DL. Mixed synapses reconcile violations of the size principle in zebrafish spinal cord. eLife 2022; 11:64063. [PMID: 36166290 PMCID: PMC9514842 DOI: 10.7554/elife.64063] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 09/12/2022] [Indexed: 11/24/2022] Open
Abstract
Mixed electrical-chemical synapses potentially complicate electrophysiological interpretations of neuronal excitability and connectivity. Here, we disentangle the impact of mixed synapses within the spinal locomotor circuitry of larval zebrafish. We demonstrate that soma size is not linked to input resistance for interneurons, contrary to the biophysical predictions of the ‘size principle’ for motor neurons. Next, we show that time constants are faster, excitatory currents stronger, and mixed potentials larger in lower resistance neurons, linking mixed synapse density to resting excitability. Using a computational model, we verify the impact of weighted electrical synapses on membrane properties, synaptic integration and the low-pass filtering and distribution of coupling potentials. We conclude differences in mixed synapse density can contribute to excitability underestimations and connectivity overestimations. The contribution of mixed synaptic inputs to resting excitability helps explain ‘violations’ of the size principle, where neuron size, resistance and recruitment order are unrelated.
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Affiliation(s)
- Evdokia Menelaou
- Department of Neurobiology, Northwestern University, Evanston, United States
| | - Sandeep Kishore
- Department of Neurobiology, Northwestern University, Evanston, United States
| | - David L McLean
- Department of Neurobiology, Northwestern University, Evanston, United States
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12
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Lu J, Zia M, Williams MJ, Jacob AL, Chung B, Sober SJ, Bakir MS. High-performance Flexible Microelectrode Array with PEDOT:PSS Coated 3D Micro-cones for Electromyographic Recording. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:5111-5114. [PMID: 36086620 PMCID: PMC10043627 DOI: 10.1109/embc48229.2022.9871052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
High signal-to-noise ratio (SNR) electromyography (EMG) recordings are essential for identifying and analyzing single motor unit activity. While high-density electrodes allow for greater spatial resolution, the smaller electrode area translates to a higher impedance and lower SNR. In this study, we developed an implantable and flexible 3D microelectrode array (MEA) with low impedance that enables high-quality EMG recording. With polyimide micro-cones realized by standard photolithography process and PEDOT:PSS coating, this design can increase effective surface area by up to 250% and significantly improve electrical performance for electrode sites with various geometric surface areas, where the electrode impedance is at most improved by 99.3%. Acute EMG activity from mice was recorded by implanting the electrodes in vivo, and we were able to detect multiple individual motor units simultaneously and with high resolution ([Formula: see text]). The charge storage capacity was measured to be 34.2 mC/cm2, indicating suitability of the electrodes for stimulation applications as well.
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Affiliation(s)
- Jiaao Lu
- Georgia Institute of Technology,Department of Electrical and Computer Engineering,Atlanta,GA,USA,30332
| | - Muneeb Zia
- Georgia Institute of Technology,Department of Electrical and Computer Engineering,Atlanta,GA,USA,30332
| | | | | | - Bryce Chung
- Emory University,Department of Biology,Atlanta,GA,USA,30322
| | | | - Muhannad S. Bakir
- Georgia Institute of Technology,Department of Electrical and Computer Engineering,Atlanta,GA,USA,30332
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13
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Basgol H, Ayhan I, Ugur E. Time Perception: A Review on Psychological, Computational, and Robotic Models. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3059045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hamit Basgol
- Department of Cognitive Science, Bogazici University, Istanbul, Turkey
| | - Inci Ayhan
- Department of Psychology, Bogazici University, Istanbul, Turkey
| | - Emre Ugur
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
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14
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Peterson SM, Singh SH, Dichter B, Scheid M, Rao RPN, Brunton BW. AJILE12: Long-term naturalistic human intracranial neural recordings and pose. Sci Data 2022; 9:184. [PMID: 35449141 PMCID: PMC9023453 DOI: 10.1038/s41597-022-01280-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 03/25/2022] [Indexed: 12/22/2022] Open
Abstract
Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints were estimated from 118 million video frames. To facilitate data exploration and reuse, we have shared AJILE12 on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard.
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Affiliation(s)
- Steven M Peterson
- University of Washington, Department of Biology, Seattle, 98195, USA.,University of Washington, eScience Institute, Seattle, USA
| | - Satpreet H Singh
- University of Washington, Department of Electrical and Computer Engineering, Seattle, USA
| | | | | | - Rajesh P N Rao
- University of Washington, Paul G. Allen School of Computer Science and Engineering, Seattle, USA.,University of Washington, Center for Neurotechnology, Seattle, USA
| | - Bingni W Brunton
- University of Washington, Department of Biology, Seattle, 98195, USA. .,University of Washington, eScience Institute, Seattle, USA.
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15
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Wimalasena LN, Braun J, Keshtkaran MR, Hofmann D, Gallego JÁ, Alessandro C, Tresch M, Miller LE, Pandarinath C. Estimating muscle activation from EMG using deep learning-based dynamical systems models. J Neural Eng 2022; 19. [PMID: 35366649 DOI: 10.1088/1741-2552/ac6369] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/01/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features. APPROACH Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation. MAIN RESULTS We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches. SIGNIFICANCE This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas.
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Affiliation(s)
- Lahiru Neth Wimalasena
- Biomedical Engineering, Emory University, 101 Woodruff Circle NE, Atlanta, Georgia, 30322-1007, UNITED STATES
| | - Jonas Braun
- Electrical and Computer Engineering, Technical University of Munich, Arcisstraße 21, Munchen, Bayern, 80333, GERMANY
| | - Mohammad Reza Keshtkaran
- Biomedical Engineering, Emory University, 101 Woodruff Circle NE, Atlanta, Georgia, 30322-1007, UNITED STATES
| | - David Hofmann
- Physics, Emory University, Math & Science Center, 400 Dowman Drive, Atlanta, Georgia, 30322-1007, UNITED STATES
| | - Juan Álvaro Gallego
- Physiology, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago, Illinois, 60611-3008, UNITED STATES
| | - Cristiano Alessandro
- Physiology, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago, Illinois, 60611-3008, UNITED STATES
| | - Matthew Tresch
- Physiology, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago, Illinois, 60611-3008, UNITED STATES
| | - Lee E Miller
- Physiology, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago, Illinois, 60611-3008, UNITED STATES
| | - Chethan Pandarinath
- Biomedical Engineering, Emory University, 101 Woodruff Circle NE, Atlanta, Georgia, 30322-1007, UNITED STATES
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16
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Hernández DG, Sober SJ, Nemenman I. Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries. eLife 2022; 11:68192. [PMID: 35315769 PMCID: PMC8989415 DOI: 10.7554/elife.68192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/19/2022] [Indexed: 11/13/2022] Open
Abstract
The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein's function based on its sequence, we still do not understand how to accurately predict an organism's behavior based on neural activity. Here we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.
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Affiliation(s)
- Damián G Hernández
- Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche, Argentina
| | - Samuel J Sober
- Department of Biology, Emory University, Atlanta, United States
| | - Ilya Nemenman
- Department of Physics, Emory University, Atlanta, United States
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17
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Shorten DP, Priesemann V, Wibral M, Lizier JT. Early lock-in of structured and specialised information flows during neural development. eLife 2022; 11:74651. [PMID: 35286256 PMCID: PMC9064303 DOI: 10.7554/elife.74651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/13/2022] [Indexed: 11/13/2022] Open
Abstract
The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing-dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.
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Affiliation(s)
- David P Shorten
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Michael Wibral
- Campus Institute for Dynamics of Biological Networks, Georg August University, Göttingen, Germany
| | - Joseph T Lizier
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
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18
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Kessler D, Carr CE, Kretzberg J, Ashida G. Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength. Front Neurosci 2022; 15:761826. [PMID: 34987357 PMCID: PMC8721039 DOI: 10.3389/fnins.2021.761826] [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: 08/20/2021] [Accepted: 11/09/2021] [Indexed: 11/24/2022] Open
Abstract
Information processing in the nervous system critically relies on temporally precise spiking activity. In the auditory system, various degrees of phase-locking can be observed from the auditory nerve to cortical neurons. The classical metric for quantifying phase-locking is the vector strength (VS), which captures the periodicity in neuronal spiking. More recently, another metric, called the correlation index (CI), was proposed to quantify the temporally reproducible response characteristics of a neuron. The CI is defined as the peak value of a normalized shuffled autocorrelogram (SAC). Both VS and CI have been used to investigate how temporal information is processed and propagated along the auditory pathways. While previous analyses of physiological data in cats suggested covariation of these two metrics, general characterization of their connection has never been performed. In the present study, we derive a rigorous relationship between VS and CI. To model phase-locking, we assume Poissonian spike trains with a temporally changing intensity function following a von Mises distribution. We demonstrate that VS and CI are mutually related via the so-called concentration parameter that determines the degree of phase-locking. We confirm that these theoretical results are largely consistent with physiological data recorded in the auditory brainstem of various animals. In addition, we generate artificial phase-locked spike sequences, for which recording and analysis parameters can be systematically manipulated. Our analysis results suggest that mismatches between empirical data and the theoretical prediction can often be explained with deviations from the von Mises distribution, including skewed or multimodal period histograms. Furthermore, temporal relations of spike trains across trials can contribute to higher CI values than predicted mathematically based on the VS. We find that, for most applications, a SAC bin width of 50 ms seems to be a favorable choice, leading to an estimated error below 2.5% for physiologically plausible conditions. Overall, our results provide general relations between the two measures of phase-locking and will aid future analyses of different physiological datasets that are characterized with these metrics.
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Affiliation(s)
- Dominik Kessler
- Computational Neuroscience, Department of Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
| | - Catherine E Carr
- Department of Biology, University of Maryland, College Park, MD, United States
| | - Jutta Kretzberg
- Computational Neuroscience, Department of Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4all, Department of Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
| | - Go Ashida
- Computational Neuroscience, Department of Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4all, Department of Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
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19
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Adam I, Maxwell A, Rößler H, Hansen EB, Vellema M, Brewer J, Elemans CPH. One-to-one innervation of vocal muscles allows precise control of birdsong. Curr Biol 2021; 31:3115-3124.e5. [PMID: 34089645 DOI: 10.1016/j.cub.2021.05.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/13/2021] [Accepted: 05/04/2021] [Indexed: 11/29/2022]
Abstract
The motor control resolution of any animal behavior is limited to the minimal force step available when activating muscles, which is set by the number and size distribution of motor units (MUs) and muscle-specific force. Birdsong is an excellent model system for understanding acquisition and maintenance of complex fine motor skills, but we know surprisingly little about how the motor pool controlling the syrinx is organized and how MU recruitment drives changes in vocal output. Here we developed an experimental paradigm to measure MU size distribution using spatiotemporal imaging of intracellular calcium concentration in cross-sections of living intact syrinx muscles. We combined these measurements with muscle stress and an in vitro syrinx preparation to determine the control resolution of fundamental frequency (fo), a key vocal parameter, in zebra finches. We show that syringeal muscles have extremely small MUs, with 40%-50% innervating ≤3 and 13%-17% innervating a single muscle fiber. Combined with the lowest specific stress (5 mN/mm2) known to skeletal vertebrate muscle, small force steps by the major fo controlling muscle provide control of 50-mHz to 7.3-Hz steps per MU. We show that the song system has the highest motor control resolution possible in the vertebrate nervous system and suggest this evolved due to strong selection on fine gradation of vocal output. Furthermore, we propose that high-resolution motor control was a key feature contributing to the radiation of songbirds that allowed diversification of song and speciation by vocal space expansion.
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Affiliation(s)
- Iris Adam
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Alyssa Maxwell
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Helen Rößler
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Emil B Hansen
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Michiel Vellema
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Jonathan Brewer
- PhyLife, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Coen P H Elemans
- Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.
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20
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Fischer P. Mechanisms of Network Interactions for Flexible Cortico-Basal Ganglia-Mediated Action Control. eNeuro 2021; 8:ENEURO.0009-21.2021. [PMID: 33883192 PMCID: PMC8205496 DOI: 10.1523/eneuro.0009-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
In humans, finely tuned γ synchronization (60-90 Hz) rapidly appears at movement onset in a motor control network involving primary motor cortex, the basal ganglia and motor thalamus. Yet the functional consequences of brief movement-related synchronization are still unclear. Distinct synchronization phenomena have also been linked to different forms of motor inhibition, including relaxing antagonist muscles, rapid movement interruption and stabilizing network dynamics for sustained contractions. Here, I will introduce detailed hypotheses about how intrasite and intersite synchronization could interact with firing rate changes in different parts of the network to enable flexible action control. The here proposed cause-and-effect relationships shine a spotlight on potential key mechanisms of cortico-basal ganglia-thalamo-cortical (CBGTC) communication. Confirming or revising these hypotheses will be critical in understanding the neuronal basis of flexible movement initiation, invigoration and inhibition. Ultimately, the study of more complex cognitive phenomena will also become more tractable once we understand the neuronal mechanisms underlying behavioral readouts.
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Affiliation(s)
- Petra Fischer
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, United Kingdom
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21
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Peterson SM, Singh SH, Wang NXR, Rao RPN, Brunton BW. Behavioral and Neural Variability of Naturalistic Arm Movements. eNeuro 2021; 8:ENEURO.0007-21.2021. [PMID: 34031100 PMCID: PMC8225404 DOI: 10.1523/eneuro.0007-21.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/27/2021] [Accepted: 05/04/2021] [Indexed: 11/21/2022] Open
Abstract
Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.
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Affiliation(s)
- Steven M Peterson
- Department of Biology, University of Washington, Seattle, Washington 98195
- eScience Institute, University of Washington, Seattle, Washington 98195
| | - Satpreet H Singh
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195
| | - Nancy X R Wang
- IBM Research, San Jose, California 95120
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195
| | - Rajesh P N Rao
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195
- Center for Neurotechnology, University of Washington, Seattle, Washington 98195
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, Washington 98195
- eScience Institute, University of Washington, Seattle, Washington 98195
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22
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Qiu Y, Wu M, Ting LH, Ueda J. Maximum Spectral Flatness Control of a Manipulandum for Human Motor System Identification. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3063964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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23
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Shorten DP, Spinney RE, Lizier JT. Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data. PLoS Comput Biol 2021; 17:e1008054. [PMID: 33872296 PMCID: PMC8084348 DOI: 10.1371/journal.pcbi.1008054] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 04/29/2021] [Accepted: 02/19/2021] [Indexed: 11/24/2022] Open
Abstract
Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.
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Affiliation(s)
- David P. Shorten
- Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Richard E. Spinney
- Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
- School of Physics and EMBL Australia Node Single Molecule Science, School of Medical Sciences, The University of New South Wales, Sydney, Australia
| | - Joseph T. Lizier
- Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
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Chagnaud BP, Perelmuter JT, Forlano PM, Bass AH. Gap junction-mediated glycinergic inhibition ensures precise temporal patterning in vocal behavior. eLife 2021; 10:e59390. [PMID: 33721553 PMCID: PMC7963477 DOI: 10.7554/elife.59390] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 02/28/2021] [Indexed: 01/30/2023] Open
Abstract
Precise neuronal firing is especially important for behaviors highly dependent on the correct sequencing and timing of muscle activity patterns, such as acoustic signaling. Acoustic signaling is an important communication modality for vertebrates, including many teleost fishes. Toadfishes are well known to exhibit high temporal fidelity in synchronous motoneuron firing within a hindbrain network directly determining the temporal structure of natural calls. Here, we investigated how these motoneurons maintain synchronous activation. We show that pronounced temporal precision in population-level motoneuronal firing depends on gap junction-mediated, glycinergic inhibition that generates a period of reduced probability of motoneuron activation. Super-resolution microscopy confirms glycinergic release sites formed by a subset of adjacent premotoneurons contacting motoneuron somata and dendrites. In aggregate, the evidence supports the hypothesis that gap junction-mediated, glycinergic inhibition provides a timing mechanism for achieving synchrony and temporal precision in the millisecond range for rapid modulation of acoustic waveforms.
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Affiliation(s)
| | | | - Paul M Forlano
- Department of Biology, Brooklyn College, City University of New YorkBrooklyn, NYUnited States
- Subprograms in Behavioral and Cognitive Neuroscience, Neuroscience, and Ecology, Evolutionary Biology and Behavior, The Graduate Center, City University of New YorkNew York, NYUnited States
| | - Andrew H Bass
- Department of Neurobiology and Behavior, Cornell UniversityIthaca, NYUnited States
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25
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Obeid D, Zavatone-Veth JA, Pehlevan C. Statistical structure of the trial-to-trial timing variability in synfire chains. Phys Rev E 2020; 102:052406. [PMID: 33327145 DOI: 10.1103/physreve.102.052406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 10/16/2020] [Indexed: 11/07/2022]
Abstract
Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit-the synfire chain. First we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a chain of single integrate-and-fire neurons. We show that this circuit's variability is well described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next we establish in simulations that these results carry over to a noisy homogeneous synfire chain. Finally, motivated by the fact that a synfire chain is thought to underlie the circuit that takes part in the control and timing of the zebra finch song, we present simulations of a biologically realistic synfire chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings and to agree with experimental observations of the song's temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.
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Affiliation(s)
- Dina Obeid
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
| | | | - Cengiz Pehlevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.,Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA
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Dickerson BH. Timing precision in fly flight control: integrating mechanosensory input with muscle physiology. Proc Biol Sci 2020; 287:20201774. [PMID: 33323088 DOI: 10.1098/rspb.2020.1774] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Animals rapidly collect and act on incoming information to navigate complex environments, making the precise timing of sensory feedback critical in the context of neural circuit function. Moreover, the timing of sensory input determines the biomechanical properties of muscles that undergo cyclic length changes, as during locomotion. Both of these issues come to a head in the case of flying insects, as these animals execute steering manoeuvres at timescales approaching the upper limits of performance for neuromechanical systems. Among insects, flies stand out as especially adept given their ability to execute manoeuvres that require sub-millisecond control of steering muscles. Although vision is critical, here I review the role of rapid, wingbeat-synchronous mechanosensory feedback from the wings and structures unique to flies, the halteres. The visual system and descending interneurons of the brain employ a spike rate coding scheme to relay commands to the wing steering system. By contrast, mechanosensory feedback operates at faster timescales and in the language of motor neurons, i.e. spike timing, allowing wing and haltere input to dynamically structure the output of the wing steering system. Although the halteres have been long known to provide essential input to the wing steering system as gyroscopic sensors, recent evidence suggests that the feedback from these vestigial hindwings is under active control. Thus, flies may accomplish manoeuvres through a conserved hindwing circuit, regulating the firing phase-and thus, the mechanical power output-of the wing steering muscles.
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Affiliation(s)
- Bradley H Dickerson
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Quarta E, Cohen EJ, Bravi R, Minciacchi D. Future Portrait of the Athletic Brain: Mechanistic Understanding of Human Sport Performance Via Animal Neurophysiology of Motor Behavior. Front Syst Neurosci 2020; 14:596200. [PMID: 33281568 PMCID: PMC7705174 DOI: 10.3389/fnsys.2020.596200] [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: 08/18/2020] [Accepted: 10/19/2020] [Indexed: 11/24/2022] Open
Abstract
Sport performances are often showcases of skilled motor control. Efforts to understand the neural processes subserving such movements may teach us about general principles of behavior, similarly to how studies on neurological patients have guided early work in cognitive neuroscience. While investigations on non-human animal models offer valuable information on the neural dynamics of skilled motor control that is still difficult to obtain from humans, sport sciences have paid relatively little attention to these mechanisms. Similarly, knowledge emerging from the study of sport performance could inspire innovative experiments in animal neurophysiology, but the latter has been only partially applied. Here, we advocate that fostering interactions between these two seemingly distant fields, i.e., animal neurophysiology and sport sciences, may lead to mutual benefits. For instance, recording and manipulating the activity from neurons of behaving animals offer a unique viewpoint on the computations for motor control, with potentially untapped relevance for motor skills development in athletes. To stimulate such transdisciplinary dialog, in the present article, we also discuss steps for the reverse translation of sport sciences findings to animal models and the evaluation of comparability between animal models of a given sport and athletes. In the final section of the article, we envision that some approaches developed for animal neurophysiology could translate to sport sciences anytime soon (e.g., advanced tracking methods) or in the future (e.g., novel brain stimulation techniques) and could be used to monitor and manipulate motor skills, with implications for human performance extending well beyond sport.
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Affiliation(s)
| | | | | | - Diego Minciacchi
- Physiological Sciences Section, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
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28
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Adam I, Elemans CPH. Increasing Muscle Speed Drives Changes in the Neuromuscular Transform of Motor Commands during Postnatal Development in Songbirds. J Neurosci 2020; 40:6722-6731. [PMID: 32487696 PMCID: PMC7455216 DOI: 10.1523/jneurosci.0111-20.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/19/2020] [Accepted: 05/21/2020] [Indexed: 01/04/2023] Open
Abstract
Progressive changes in vocal behavior over the course of vocal imitation leaning are often attributed exclusively to developing neural circuits, but the effects of postnatal body changes remain unknown. In songbirds, the syrinx transforms song system motor commands into sound and exhibits changes during song learning. Here we test the hypothesis that the transformation from motor commands to force trajectories by syringeal muscles functionally changes over vocal development in zebra finches. Our data collected in both sexes show that, only in males, muscle speed significantly increases and that supralinear summation occurs and increases with muscle contraction speed. Furthermore, we show that previously reported submillisecond spike timing in the avian cortex can be resolved by superfast syringeal muscles and that the sensitivity to spike timing increases with speed. Because motor neuron and muscle properties are tightly linked, we make predictions on the boundaries of the yet unknown motor code that correspond well with cortical activity. Together, we show that syringeal muscles undergo essential transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the song system motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.SIGNIFICANCE STATEMENT Fine motor skill learning typically occurs in a postnatal period when the brain is learning to control a body that is changing dramatically due to growth and development. How the developing body influences motor code formation and vice versa remains largely unknown. Here we show that vocal muscles in songbirds undergo critical transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.
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Affiliation(s)
- Iris Adam
- University of Southern Denmark, Department of Biology, 5230 Odense M, Denmark
| | - Coen P H Elemans
- University of Southern Denmark, Department of Biology, 5230 Odense M, Denmark
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Howard CW, Toossi A, Mushahwar VK. Variety Is the Spice of Life: Positive and Negative Effects of Noise in Electrical Stimulation of the Nervous System. Neuroscientist 2020; 27:529-543. [DOI: 10.1177/1073858420951155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Noisy stimuli may hold the key for optimal electrical stimulation of the nervous system. Possible mechanisms of noise’s impact upon neuronal function are discussed, including intracellular, extracellular, and systems-level mechanisms. Specifically, channel resonance, stochastic resonance, high conductance states, and network binding are investigated. These mechanisms are examined and possible directions of growth for the field are discussed, with examples of applications provided from the fields of deep brain stimulation or spinal cord injury. Together, this review highlights the theoretical basis and evidence base for the use of noise to enhance current stimulation paradigms of the nervous system.
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Affiliation(s)
- Calvin W. Howard
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada
| | - Amirali Toossi
- Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Vivian K. Mushahwar
- Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
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30
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Mukherjee R, Caron DP, Edson T, Trimmer BA. The control of nocifensive movements in the caterpillar Manduca sexta. J Exp Biol 2020; 223:jeb221010. [PMID: 32647020 DOI: 10.1242/jeb.221010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 07/01/2020] [Indexed: 11/20/2022]
Abstract
In response to a noxious stimulus on the abdomen, caterpillars lunge their head towards the site of stimulation. This nocifensive 'strike' behavior is fast (∼0.5 s duration), targeted and usually unilateral. It is not clear how the fast strike movement is generated and controlled, because caterpillar muscle develops peak force relatively slowly (∼1 s) and the baseline hemolymph pressure is low (<2 kPa). Here, we show that strike movements are largely driven by ipsilateral muscle activation that propagates from anterior to posterior segments. There is no sustained pre-strike muscle activation that would be expected for movements powered by the rapid release of stored elastic energy. Although muscle activation on the ipsilateral side is correlated with segment shortening, activity on the contralateral side consists of two phases of muscle stimulation and a marked decline between them. This decrease in motor activity precedes rapid expansion of the segment on the contralateral side, presumably allowing the body wall to stretch more easily. The subsequent increase in contralateral motor activation may slow or stabilize movements as the head reaches its target. Strike behavior is therefore a controlled fast movement involving the coordination of muscle activity on each side and along the length of the body.
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Affiliation(s)
- Ritwika Mukherjee
- Tufts University, Department of Biology, 200 Boston Avenue, Suite 2600, MA 02155, USA
| | - Daniel P Caron
- Tufts University, Department of Biology, 200 Boston Avenue, Suite 2600, MA 02155, USA
| | - Timothy Edson
- Department of Chemistry and Biochemistry, Bates College, 2 Andrews Road, Lewiston, ME 04240, USA
| | - Barry A Trimmer
- Tufts University, Department of Biology, 200 Boston Avenue, Suite 2600, MA 02155, USA
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31
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Issa JB, Tocker G, Hasselmo ME, Heys JG, Dombeck DA. Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time. Annu Rev Neurosci 2020; 43:73-93. [PMID: 31961765 PMCID: PMC7351603 DOI: 10.1146/annurev-neuro-101419-011117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.
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Affiliation(s)
- John B Issa
- Department of Neurobiology, Northwestern University, Evanston, Illinois 60208, USA;
| | - Gilad Tocker
- Department of Neurobiology, Northwestern University, Evanston, Illinois 60208, USA;
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215, USA
| | - James G Heys
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, Utah 84112, USA
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, Illinois 60208, USA;
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Abstract
Behavior is readily classified into patterns of movements with inferred common goals-actions. Goals may be discrete; movements are continuous. Through the careful study of isolated movements in laboratory settings, or via introspection, it has become clear that animals can exhibit exquisite graded specification to their movements. Moreover, graded control can be as fundamental to success as the selection of which action to perform under many naturalistic scenarios: a predator adjusting its speed to intercept moving prey, or a tool-user exerting the perfect amount of force to complete a delicate task. The basal ganglia are a collection of nuclei in vertebrates that extend from the forebrain (telencephalon) to the midbrain (mesencephalon), constituting a major descending extrapyramidal pathway for control over midbrain and brainstem premotor structures. Here we discuss how this pathway contributes to the continuous specification of movements that endows our voluntary actions with vigor and grace.
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Affiliation(s)
- Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA;
| | - Luke T Coddington
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA;
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA;
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33
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Neural Mechanisms Underlying High-Frequency Vestibulocollic Reflexes In Humans And Monkeys. J Neurosci 2020; 40:1874-1887. [PMID: 31959700 DOI: 10.1523/jneurosci.1463-19.2020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 12/30/2019] [Accepted: 01/07/2020] [Indexed: 01/16/2023] Open
Abstract
The vestibulocollic reflex is a compensatory response that stabilizes the head in space. During everyday activities, this stabilizing response is evoked by head movements that typically span frequencies from 0 to 30 Hz. Transient head impacts, however, can elicit head movements with frequency content up to 300-400 Hz, raising the question whether vestibular pathways contribute to head stabilization at such high frequencies. Here, we first established that electrical vestibular stimulation modulates human neck motor unit (MU) activity at sinusoidal frequencies up to 300 Hz, but that sensitivity increases with frequency up to a low-pass cutoff of ∼70-80 Hz. To examine the neural substrates underlying the low-pass dynamics of vestibulocollic reflexes, we then recorded vestibular afferent responses to the same electrical stimuli in monkeys. Vestibular afferents also responded to electrical stimuli up to 300 Hz, but in contrast to MUs their sensitivity increased with frequency up to the afferent resting firing rate (∼100-150 Hz) and at higher frequencies afferents tended to phase-lock to the vestibular stimulus. This latter nonlinearity, however, was not transmitted to neck motoneurons, which instead showed minimal phase-locking that decreased at frequencies >75 Hz. Similar to human data, we validated that monkey muscle activity also exhibited low-pass filtered vestibulocollic reflex dynamics. Together, our results show that neck MUs are activated by high-frequency signals encoded by primary vestibular afferents, but undergo low-pass filtering at intermediate stages in the vestibulocollic reflex. These high-frequency contributions to vestibular-evoked neck muscle responses could stabilize the head during unexpected head transients.SIGNIFICANCE STATEMENT Vestibular-evoked neck muscle responses rely on accurate encoding and transmission of head movement information to stabilize the head in space. Unexpected transient events, such as head impacts, are likely to push the limits of these neural pathways since their high-frequency features (0-300 Hz) extend beyond the frequency bandwidth of head movements experienced during everyday activities (0-30 Hz). Here, we demonstrate that vestibular primary afferents encode high-frequency stimuli through frequency-dependent increases in sensitivity and phase-locking. When transmitted to neck motoneurons, these signals undergo low-pass filtering that limits neck motoneuron phase-locking in response to stimuli >75 Hz. This study provides insight into the neural dynamics producing vestibulocollic reflexes, which may respond to high-frequency transient events to stabilize the head.
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Putney J, Conn R, Sponberg S. Precise timing is ubiquitous, consistent, and coordinated across a comprehensive, spike-resolved flight motor program. Proc Natl Acad Sci U S A 2019; 116:26951-26960. [PMID: 31843904 PMCID: PMC6936677 DOI: 10.1073/pnas.1907513116] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Sequences of action potentials, or spikes, carry information in the number of spikes and their timing. Spike timing codes are critical in many sensory systems, but there is now growing evidence that millisecond-scale changes in timing also carry information in motor brain regions, descending decision-making circuits, and individual motor units. Across all of the many signals that control a behavior, how ubiquitous, consistent, and coordinated are spike timing codes? Assessing these open questions ideally involves recording across the whole motor program with spike-level resolution. To do this, we took advantage of the relatively few motor units controlling the wings of a hawk moth, Manduca sexta. We simultaneously recorded nearly every action potential from all major wing muscles and the resulting forces in tethered flight. We found that timing encodes more information about turning behavior than spike count in every motor unit, even though there is sufficient variation in count alone. Flight muscles vary broadly in function as well as in the number and timing of spikes. Nonetheless, each muscle with multiple spikes consistently blends spike timing and count information in a 3:1 ratio. Coding strategies are consistent. Finally, we assess the coordination of muscles using pairwise redundancy measured through interaction information. Surprisingly, not only are all muscle pairs coordinated, but all coordination is accomplished almost exclusively through spike timing, not spike count. Spike timing codes are ubiquitous, consistent, and essential for coordination.
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Affiliation(s)
- Joy Putney
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Rachel Conn
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- Neuroscience Program, Emory University, Atlanta, GA 30322
| | - Simon Sponberg
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA 30332
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
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Abstract
The neural coding metaphor is so ubiquitous that we tend to forget its metaphorical nature. What do we mean when we assert that neurons encode and decode? What kind of causal and representational model of the brain does the metaphor entail? What lies beneath the neural coding metaphor, I argue, is a bureaucratic model of the brain.
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Mathis A, Pack AR, Maeda RS, McDougle SD. Highlights from the 29th Annual Meeting of the Society for the Neural Control of Movement. J Neurophysiol 2019; 122:1777-1783. [PMID: 31461364 PMCID: PMC6843106 DOI: 10.1152/jn.00484.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/21/2019] [Accepted: 08/21/2019] [Indexed: 11/22/2022] Open
Affiliation(s)
- Alexander Mathis
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, Massachusetts
| | - Andrea R Pack
- Department of Biology, Emory University, Atlanta, Georgia
| | - Rodrigo S Maeda
- Brain and Mind Institute, Western University, London, Ontario, Canada
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Psychology, Western University, London, Ontario, Canada
| | - Samuel D McDougle
- Department of Psychology, University of California, Berkeley, California
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37
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Neveln ID, Tirumalai A, Sponberg S. Information-based centralization of locomotion in animals and robots. Nat Commun 2019; 10:3655. [PMID: 31409794 PMCID: PMC6692360 DOI: 10.1038/s41467-019-11613-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 07/22/2019] [Indexed: 11/09/2022] Open
Abstract
The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in freely running cockroaches. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle's leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems.
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Affiliation(s)
- Izaak D Neveln
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Amoolya Tirumalai
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Simon Sponberg
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
- School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
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38
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Abstract
Grip force has been studied widely in a variety of interaction and movement tasks, however, not much is known about the timing of the grip force control in preparation for interaction with objects. For example, it is unknown whether and how the temporal preparation for a collision is related to (the prediction of) the impact load. To study this question, we examined the anticipative timing of the grip force in preparation for impact loads. We designed a collision task with different types of load forces in a controlled virtual environment. Participants interacted with a robotic device (KINARM, BKIN Technologies, Kingston) whose handles were equipped with force sensors which the participants held in precision grip. Representations of the hand and objects were visually projected on a virtual reality display and forces were applied onto the participant's hand to simulate a collision with the virtual objects. The collisions were alternating between the two hands to allow transfer and learning between the hands. The results show that there is immediate transfer of object information between the two hands, since the grip force levels are (almost) fully adjusted after one collision with the opposite hand. The results also show that the grip force levels are nicely adjusted based on the mass and stiffness of the object. Surprisingly, the temporal onset of the grip force build up did not depend on the impact load, so that participants avoid slippage by adjusting the other grip force characteristics (e.g., grip force level and rate of change), therefore considering these self-imposed timing constraints. With the use of catch trials, for which no impact occurred, we further analyzed the temporal profile of the grip force. The catch trial data showed that the timing of the grip force peak is also independent of the impact load and its timing, which suggests a time-locked planning of the complete grip force profile.
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39
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Vocal Motor Performance in Birdsong Requires Brain-Body Interaction. eNeuro 2019; 6:ENEURO.0053-19.2019. [PMID: 31182473 PMCID: PMC6595438 DOI: 10.1523/eneuro.0053-19.2019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/22/2019] [Accepted: 05/24/2019] [Indexed: 02/06/2023] Open
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41
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