Identifying features in spike trains using binless similarity measures

TL;DR

This paper introduces two novel binless similarity measures for spike trains, improving the detection of neural encoding features like firing rate, bursts, silence, and synchrony, and compares their performance with existing methods.

Contribution

The paper proposes two new binless similarity measures for spike trains and evaluates their effectiveness against existing measures in capturing neural features.

Findings

New measures outperform existing ones in detecting synchrony.

Improved sensitivity to bursts and silence periods.

Enhanced ability to identify firing rate differences.

Abstract

Neurons in the central nervous system communicate with each other with the help of series of Action Potentials, or spike trains. Various studies have shown that neurons encode information in different features of spike trains, such as the fine temporal structure, mean firing rate, synchrony etc. An important step in understanding the encoding of information by neurons, is to obtain a reliable measure of correlation between different spike trains. In this paper, two new binless similarity measures for spike trains are proposed. The performance of the new measures are compared with some existing measures in their ability to detect important features of spike trains, such as their firing rate, sensitivity to bursts and common periods of silence and detecting synchronous activity.