Information measure for analyzing specific spiking patterns and applications to LGN bursts

Abstract

Neural spiking responses can include a variety of spiking patterns. However, neither the mere presence of the patterns nor the pattern's frequency indicates that the pattern conveys distinct stimulus information. Here, we present an in-depth analysis of a Pattern Information measure, which quantifies how informative it is to distinguish a particular pattern of spikes from either a single spike or an another pattern. (1) We show how a shuffle-controlled estimation method minimizes the impact of sampling bias. (2) We describe how the Pattern Information could arise from time-varying firing rates, and we demonstrate an analysis to determine whether Pattern Information associated with a particular pattern captures structure not contained in the time-varying firing rate. (3) Because patterns may contain several spikes or inter-spike intervals, we extend the Pattern Information measure to determine whether the complete pattern carries information distinct from sub-patterns containing only a fraction of these spikes or intervals. (4) The Pattern Information is applied to determine whether a plurality of patterns carry distinct stimulus information from one another. In particular, we demonstrate these concepts using data from cells of the lateral geniculate nucleus (LGN), thereby extending previous analysis demonstrating that distinguishes between bursts of spikes and single spikes providing visual information.