Decomposition of neuronal assembly activity via empirical de-Poissonization

TL;DR

This paper introduces a statistical method to estimate neuronal assembly activity by analyzing compound Poisson processes, enabling detection of joint spike events in neural data without requiring single-neuron recordings.

Contribution

It develops a novel empirical de-Poissonization technique to estimate the jump measure of a compound Poisson process from population spike data, addressing challenges in neural interaction analysis.

Findings

Successfully estimates neuronal assembly activity from population data

Detects higher-order joint spike events without isolating single neurons

Provides a practical statistical tool for neural data analysis

Abstract

Consider a compound Poisson process with jump measure $\nu$ supported by finitely many positive integers. We propose a method for estimating $\nu$ from a single, equidistantly sampled trajectory and develop associated statistical procedures. The problem is motivated by the question whether nerve cells in the brain exhibit higher-order interactions in their firing patterns. According to the neuronal assembly hypothesis (Hebb [13]), synchronization of action potentials across neurons of different groups is considered a signature of assembly activity, but it was found notoriously difficult to demonstrate it in recordings of neuronal activity. Our approach based on a compound Poisson model allows to detect the presence of joint spike events of any order using only population spike count samples, thus bypassing both the ``curse of dimensionality'' and the need to isolate single-neuron spike trains in population signals.