  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.

Neurons and Cognition;Signal Processing