Literature DB >> 21835350

Single-trial neural correlates of arm movement preparation.

Afsheen Afshar1, Gopal Santhanam, Byron M Yu, Stephen I Ryu, Maneesh Sahani, Krishna V Shenoy.   

Abstract

The process by which neural circuitry in the brain plans and executes movements is not well understood. Until recently, most available data were limited either to single-neuron electrophysiological recordings or to measures of aggregate field or metabolism. Neither approach reveals how individual neurons' activities are coordinated within the population, and thus inferences about how the neural circuit forms a motor plan for an upcoming movement have been indirect. Here we build on recent advances in the measurement and description of population activity to frame and test an "initial condition hypothesis" of arm movement preparation and initiation. This hypothesis leads to a model in which the timing of movements may be predicted on each trial using neurons' moment-by-moment firing rates and rates of change of those rates. Using simultaneous microelectrode array recordings from premotor cortex of monkeys performing delayed-reach movements, we compare such single-trial predictions to those of other theories. We show that our model can explain approximately 4-fold more arm-movement reaction-time variance than the best alternative method. Thus, the initial condition hypothesis elucidates a view of the relationship between single-trial preparatory neural population dynamics and single-trial behavior.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21835350      PMCID: PMC3155684          DOI: 10.1016/j.neuron.2011.05.047

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  21 in total

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Authors:  M Weinrich; S P Wise
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Journal:  Science       Date:  1996-10-18       Impact factor: 47.728

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  86 in total

Review 1.  Dimensionality reduction for large-scale neural recordings.

Authors:  John P Cunningham; Byron M Yu
Journal:  Nat Neurosci       Date:  2014-08-24       Impact factor: 24.884

2.  Heterogeneous attractor cell assemblies for motor planning in premotor cortex.

Authors:  Maurizio Mattia; Pierpaolo Pani; Giovanni Mirabella; Stefania Costa; Paolo Del Giudice; Stefano Ferraina
Journal:  J Neurosci       Date:  2013-07-03       Impact factor: 6.167

3.  DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.

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4.  Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex.

Authors:  Samuel A Neymotin; George L Chadderdon; Cliff C Kerr; Joseph T Francis; William W Lytton
Journal:  Neural Comput       Date:  2013-09-18       Impact factor: 2.026

5.  Response to "Fallacies of Mice Experiments".

Authors:  Zhenyu Gao; Alyse M Thomas; Michael N Economo; Amada M Abrego; Karel Svoboda; Chris I De Zeeuw; Nuo Li
Journal:  Neuroinformatics       Date:  2019-10

Review 6.  Revisiting the role of persistent neural activity during working memory.

Authors:  Kartik K Sreenivasan; Clayton E Curtis; Mark D'Esposito
Journal:  Trends Cogn Sci       Date:  2014-01-14       Impact factor: 20.229

7.  An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.

Authors:  David A Borton; Ming Yin; Juan Aceros; Arto Nurmikko
Journal:  J Neural Eng       Date:  2013-02-21       Impact factor: 5.379

8.  Behavioral and neural signatures of readiness to initiate a learned motor sequence.

Authors:  Raghav Rajan; Allison J Doupe
Journal:  Curr Biol       Date:  2012-12-13       Impact factor: 10.834

9.  Neural dynamics of reaching following incorrect or absent motor preparation.

Authors:  K Cora Ames; Stephen I Ryu; Krishna V Shenoy
Journal:  Neuron       Date:  2014-01-22       Impact factor: 17.173

10.  Intention estimation in brain-machine interfaces.

Authors:  Joline M Fan; Paul Nuyujukian; Jonathan C Kao; Cynthia A Chestek; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2014-02       Impact factor: 5.379

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