Sparse convolutional coding for neuronal ensemble identification

TL;DR

This paper introduces a sparse convolutional coding algorithm for identifying neuronal cell ensembles with temporal structure, outperforming previous methods especially in noisy and overlapping scenarios.

Contribution

The paper presents a novel sparse convolutional coding algorithm capable of detecting temporally structured neuronal ensembles, advancing beyond existing methods like PCA.

Findings

Outperforms previous methods in synthetic datasets

Accurately identifies temporal cell ensembles with overlaps

Effective in high-noise conditions

Abstract

Cell ensembles, originally proposed by Donald Hebb in 1949, are subsets of synchronously firing neurons and proposed to explain basic firing behavior in the brain. Despite having been studied for many years no conclusive evidence has been presented yet for their existence and involvement in information processing such that their identification is still a topic of modern research, especially since simultaneous recordings of large neuronal population have become possible in the past three decades. These large recordings pose a challenge for methods allowing to identify individual neurons forming cell ensembles and their time course of activity inside the vast amounts of spikes recorded. Related work so far focused on the identification of purely simulta- neously firing neurons using techniques such as Principal Component Analysis. In this paper we propose a new algorithm based on sparse convolution coding which is also able to find ensembles with temporal structure. Application of our algorithm to synthetically generated datasets shows that it outperforms previous work and is able to accurately identify temporal cell ensembles even when those contain overlapping neurons or when strong background noise is present.