Edinburgh Research Explorer

A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts

Research output: Contribution to journalArticle

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Final published version, 764 KB, PDF document

    Licence: Creative Commons: Attribution (CC-BY)

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002539
Original languageEnglish
Article numbere1002539
Pages (from-to)1-12
Number of pages12
JournalPLoS Computational Biology
Volume8
Issue number6
DOIs
Publication statusPublished - 7 Jun 2012

Abstract

Author Summary Populations of neurons signal information by their joint activity. Dependencies between the activity of multiple neurons are typically described by the linear correlation coefficient. However, this description of the dependencies is not complete. Dependencies beyond the linear correlation coefficient, so-called higher-order correlations, are often neglected because too many experimental samples are required in order to estimate them reliably. Evaluating the importance of higher-order correlations for the neural representation has therefore been notoriously hard. We devise a statistical test that can quantify evidence for higher-order correlations without estimating higher-order correlations directly. The test yields reliable results even when the number of experimental samples is small. The power of the method is demonstrated on data which were recorded from a population of neurons in the primary visual cortex of cat during an adaptation experiment. We show that higher-order correlations can have a substantial impact on the encoded stimulus information which, moreover, is modulated by stimulus adaptation.

Download statistics

No data available

ID: 44998107