Neural population decoding in short-time windows

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

Abstract

External information is encoded in spiking activities of neural population. The present study investigates the performance of population decoding in a short-time window. Two decoding strategies, namely, maximum likelihood inference and template-matching, are explored.We find that in a short-timewindow, two methods are not efficient and that their errors satisfy the Cauchy distributions. As expected, maximum likelihood inference outperforms template-matching asymptotically. However, in a very short time window, template-matching has smaller decoding errors than maximum likelihood inference. The implication of this result is discussed.

Original languageEnglish (US)
Title of host publicationIntelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers
Pages56-63
Number of pages8
DOIs
StatePublished - 2013
Externally publishedYes
Event3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 - Nanjing, China
Duration: Oct 15 2012Oct 17 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7751 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012
Country/TerritoryChina
CityNanjing
Period10/15/1210/17/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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