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A Simple Algorithm for Averaging Spike Trains

Hannah Julienne12 and Conor Houghton13*

Author Affiliations

1 School of Mathematics, Trinity College Dublin, Dublin, Ireland

2 School of Physiology & Pharmacology, University of Bristol, Medical Sciences Building, University Walk, Bristol, BS8 1TD, England

3 Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1TD, England

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The Journal of Mathematical Neuroscience 2013, 3:3  doi:10.1186/2190-8567-3-3

Published: 25 February 2013


Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.