Algorithm for calculating theoretical probabilities of patterns generated by sequential inequality testing.

Abstract

Temporal patterns of extracellularly monitored single neuronal impulses or 'spike' trains can be viewed as stochastic point processes that carry information from one neuron to another. There are indications that the dependencies among sequential spike intervals, if treated as sequential inequality patterns, encompass much more than 7 spike intervals. Hence, to fill the gap between the available knowledge and the experimental need, a limited stochastic model of inequality patterns was reviewed and its inherent symmetries were explored. The symmetric attributes of the model, based on three through seven spike intervals, led to an algorithm which allows one to readily compute the theoretical distribution of inequality patterns of considerable complexity and length suitable for studying neuronal responses and other phenomena.