Estimation of neuronal interaction graph from spike train data

TL;DR

This paper develops a new method to estimate neuronal interaction graphs from spike train data, addressing challenges of small sample sizes and partial observations, validated through simulations on neurophysiological data.

Contribution

It introduces an improved procedure for inferring directed neuronal graphs that handles small samples and partial network observations, building on previous methods.

Findings

Enhanced accuracy in graph estimation with small sample sizes

Effective handling of partially observed neuronal networks

Validated approach on real neurophysiological data

Abstract

One of the main current issues in Neurobiology concerns the understanding of interrelated spiking activity among multineuronal ensembles and differences between stimulus-driven and spontaneous activity in neurophysiological experiments. Multi electrode array recordings that are now commonly used monitor neuronal activity in the form of spike trains from many well identified neurons. A basic question when analyzing such data is the identification of the directed graph describing "synaptic coupling" between neurons. In this article we deal with this matter working with a high quality multielectrode array recording dataset (Pouzat et al., 2015) from the first olfactory relay of the locust, $Schistocerca$ $americana$. From a mathematical point of view this paper presents two novelties. First we propose a procedure allowing to deal with the small sample sizes met in actual datasets. Moreover we address the sensitive case of partially observed networks. Our starting point is the procedure introduced in Duarte et al. (2016). We evaluate the performance of both original and improved procedures through simulation studies, which are also used for parameter tuning and for exploring the effect of recording only a small subset of the neurons of a network.