Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

TL;DR

This paper introduces a faster, accurate method using Hellinger distance for analyzing neuronal spike train similarities, enabling efficient clustering of neurons and revealing functional heterogeneity, especially in short sequences.

Contribution

The paper presents a novel Hellinger distance-based measure that converges faster than existing methods for spike train similarity analysis, suitable for short sequences.

Findings

Identified two non-overlapping classes of neuronal spike trains.

Demonstrated faster convergence compared to existing techniques.

Detected functional heterogeneity in neuronal responses.

Abstract

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns.