Uncovering the neural code using a rat model during a learning control task

Chenhui Yang, Hongwei Mao, Yuan Yuan, Bing Cheng, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

How neuronal firing activities encode meaningful behavior is an ultimate challenge to neuroscientists. To make the problem tractable, we use a rat model to elucidate how an ensemble of single neuron firing events leads to conscious, goal-directed movement and control. This study discusses findings based on single unit, multi-channel simultaneous recordings from rats frontal areas while they learned to perform a decision and control task. To study neural firing activities, first and foremost we needed to identify single unit firing action potentials, or perform spike sorting prior to any analysis on the ensemble of neural activities. After that, we studied cortical neural firing rates to characterize their changes as rats learned a directional paddle control task. Single units from the rat's frontal areas were inspected for their possible encoding mechanism of directional and sequential movement parameters. Our results entail both high level statistical snapshots of the neural data and more detailed neuronal roles in relation to rat's learning control behavior.

Original languageEnglish (US)
Title of host publicationAdvances in Computational Intelligence - IEEE World Congress on Computational Intelligence, WCCI 2012, Plenary/Invited Lectures
Pages261-279
Number of pages19
DOIs
StatePublished - Aug 20 2012
Event2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: Jun 10 2012Jun 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7311 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2012 IEEE World Congress on Computational Intelligence, WCCI 2012
CountryAustralia
CityBrisbane, QLD
Period6/10/126/15/12

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yang, C., Mao, H., Yuan, Y., Cheng, B., & Si, J. (2012). Uncovering the neural code using a rat model during a learning control task. In Advances in Computational Intelligence - IEEE World Congress on Computational Intelligence, WCCI 2012, Plenary/Invited Lectures (pp. 261-279). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7311 LNCS). https://doi.org/10.1007/978-3-642-30687-7_13