Literature DB >> 23658171

"Master" neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task.

Pierre-Jean Arduin1, Yves Frégnac, Daniel E Shulz, Valérie Ego-Stengel.   

Abstract

Operant control of a prosthesis by neuronal cortical activity is one of the successful strategies for implementing brain-machine interfaces (BMI), by which the subject learns to exert a volitional control of goal-directed movements. However, it remains unknown if the induced brain circuit reorganization affects preferentially the conditioned neurons whose activity controlled the BMI actuator during training. Here, multiple extracellular single-units were recorded simultaneously in the motor cortex of head-fixed behaving rats. The firing rate of a single neuron was used to control the position of a one-dimensional actuator. Each time the firing rate crossed a predefined threshold, a water bottle moved toward the rat, until the cumulative displacement of the bottle allowed the animal to drink. After a learning period, most (88%) conditioned neurons raised their activity during the trials, such that the time to reward decreased across sessions: the conditioned neuron fired strongly, reliably and swiftly after trial onset, although no explicit instruction in the learning rule imposed a fast neuronal response. Moreover, the conditioned neuron fired significantly earlier and more strongly than nonconditioned neighboring neurons. During the first training sessions, an increase in firing rate variability was seen only for the highly conditionable neurons. This variability then decreased while the conditioning effect increased. These findings suggest that modifications during training target preferentially the neuron chosen to control the BMI, which acts then as a "master" neuron, leading in time the reconfiguration of activity in the local cortical network.

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Mesh:

Year:  2013        PMID: 23658171      PMCID: PMC6619624          DOI: 10.1523/JNEUROSCI.2744-12.2013

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  17 in total

1.  Delayed grip relaxation and altered modulation of intracortical inhibition with aging.

Authors:  Binal Motawar; James W Stinear; Abigail W Lauer; Viswanathan Ramakrishnan; Na Jin Seo
Journal:  Exp Brain Res       Date:  2015-12-21       Impact factor: 1.972

2.  A rodent brain-machine interface paradigm to study the impact of paraplegia on BMI performance.

Authors:  Nathaniel R Bridges; Michael Meyers; Jonathan Garcia; Patricia A Shewokis; Karen A Moxon
Journal:  J Neurosci Methods       Date:  2018-05-31       Impact factor: 2.390

3.  Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning.

Authors:  Tanuj Gulati; Dhakshin S Ramanathan; Chelsea C Wong; Karunesh Ganguly
Journal:  Nat Neurosci       Date:  2014-07-06       Impact factor: 24.884

4.  Emergence of reproducible spatiotemporal activity during motor learning.

Authors:  Andrew J Peters; Simon X Chen; Takaki Komiyama
Journal:  Nature       Date:  2014-05-04       Impact factor: 49.962

Review 5.  Parsing learning in networks using brain-machine interfaces.

Authors:  Amy L Orsborn; Bijan Pesaran
Journal:  Curr Opin Neurobiol       Date:  2017-08-24       Impact factor: 6.627

Review 6.  Learning in the Rodent Motor Cortex.

Authors:  Andrew J Peters; Haixin Liu; Takaki Komiyama
Journal:  Annu Rev Neurosci       Date:  2017-03-31       Impact factor: 12.449

Review 7.  Neurofeedback and neural self-regulation: a new perspective based on allostasis.

Authors:  Arash Mirifar; Andreas Keil; Felix Ehrlenspiel
Journal:  Rev Neurosci       Date:  2022-02-07       Impact factor: 4.703

8.  Volitional control of individual neurons in the human brain.

Authors:  Kramay Patel; Chaim N Katz; Suneil K Kalia; Milos R Popovic; Taufik A Valiante
Journal:  Brain       Date:  2021-12-31       Impact factor: 15.255

9.  Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning.

Authors:  Katherine Derosier; Tess L Veuthey; Karunesh Ganguly
Journal:  J Neurosci       Date:  2021-11-03       Impact factor: 6.709

Review 10.  Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

Authors:  Matthew D Golub; Steven M Chase; Aaron P Batista; Byron M Yu
Journal:  Curr Opin Neurobiol       Date:  2016-01-19       Impact factor: 6.627

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