Information transmission in oscillatory neural activity

TL;DR

This paper introduces new modeling and analysis tools for quantifying information transmission in oscillatory neural activity, especially when phase locking to stimuli is weak or absent.

Contribution

It proposes a Gamma process-based model for oscillatory spike trains and a generalized information assessment method for such data.

Findings

Model reproduces inter-spike interval histograms and phase locking features.

Tools applied successfully to cat lateral geniculate nucleus recordings.

Enhanced understanding of information flow in non-locked neural oscillations.

Abstract

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.