Statistical significance of sequential firing patterns in multi-neuronal spike trains

Casey O. Diekman, P. S. Sastry, K. P. Unnikrishnan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Original languageEnglish (US)
Pages (from-to)279-284
Number of pages6
JournalJournal of Neuroscience Methods
Volume182
Issue number2
DOIs
StatePublished - Sep 15 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • Multi-neuron
  • Precise timing
  • Spike train
  • Statistical significance
  • Temporal firing patterns

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