Paper Info
Title
Conditional probability based significance tests for sequential patterns in multi-neuronal spike trains
Abstract
In this paper we consider the problem of detecting statistically significant sequential patterns in multi-neuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data mining scheme to efficiently discover such patterns which are frequent in the sense that the count of non-overlapping occurrences of the pattern in the data stream is above a threshold. Here we propose a method to determine the statistical significance of these repeating patterns and to set the thresholds automatically. The novelty of our approach is that we use a compound null hypothesis that includes not only models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to calculate the mean and variance of the count for any pattern. Using this we derive a test of significance for rejecting such a null hypothesis. This also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated from a non-homogeneous Poisson model with embedded dependencies.
Year
Venue
Keywords
2008
Clinical Orthopaedics and Related Research
upper bound,poisson model,data mining,probabilistic model,conditional probability,statistical significance
DocType
Volume
Citations 
Journal
abs/0808.3
4
PageRank 
References 
Authors
0.44
0
2
Name
Order
Citations
PageRank
P. S. Sastry174157.27
K. P. Unnikrishnan229923.21