A new framework for Euclidean summary statistics in the neural spike train space

TL;DR

This paper introduces a Euclidean-like framework for defining mean spike trains, enabling more accurate and consistent averaging of neural spike data, which improves noise removal and decoding in neural analysis.

Contribution

It proposes a novel Euclidean-like metric-based method for mean spike train computation, addressing non-uniqueness issues of previous metrics and demonstrating practical advantages.

Findings

The new mean spike train accurately represents average neural activity patterns.

The framework improves noise removal in neural recordings.

It enhances decoding performance in neural data analysis.

Abstract

Statistical analysis and inference on spike trains is one of the central topics in the neural coding. It is of great interest to understand the underlying structure of given neural data. Based on the metric distances between spike trains, recent investigations have introduced the notion of an average or prototype spike train to characterize the template pattern in the neural activity. However, as those metrics lack certain Euclidean properties, the defined averages are nonunique, and do not share the conventional properties of a mean. In this article, we propose a new framework to define the mean spike train where we adopt a Euclidean-like metric from an $L^p$ family. We demonstrate that this new mean spike train properly represents the average pattern in the conventional fashion, and can be effectively computed using a theoretically-proven convergent procedure. We compare this mean with other spike train averages and demonstrate its superiority. Furthermore, we apply the new framework in a recording from rodent geniculate ganglion, where background firing activity is a common issue for neural coding. We show that the proposed mean spike train can be utilized to remove the background noise and improve decoding performance.