Title: Improving the Discriminability of Similarity Measures for Small Sets of Spike Trains
Abstract: Event Abstract Back to Event Improving the Discriminability of Similarity Measures for Small Sets of Spike Trains Richard Naud1*, Felipe Gerhard1, Skander Mensi1 and Wulfram Gerstner1 1 Ecole Polytechnique Fédérale de Lausanne, Brain Mind Institute, Switzerland Multiple types of measures have been developed to measure the similarity between two spike trains. These were extensively used to classify neuron responses according to stimuli and to validate mathematical models that predict the spike times. Here we analyze the existing similarity measures in the light of trial-to-trial variability. Using a small set of spike train it is often impossible to discriminate correctly between different generative processes. In particular we find that many measures cannot discriminate appropriately for shifts in overall firing intensity or for the amount of jitter in the spike timing. We find that it is possible to modify some of the existing measures by taking into account the variance of the measure across spike trains from the same set. In so doing we remove a sample bias and we find that it is possible to discriminate correctly in all cases. Finally, we demonstrate that without sample bias compensation the similarity of real neurons with spiking neuron models having low stochasticity will be overrated. Figure 1: Discriminability of spike trains generated with a spike-response model with escape noise as a function of model parameters: a) stochastic scaling (a.k.a. temperature), b) adaptation time constant and c) time constant of the input filter. Negative discriminability should never appear since it means that the spike trains are closer to a process that was generated with different statistics than to spike trains generated with the same statistics. Here we compare the Victor-and-Purpura measure with the unbiased measured discussed in the poster. Only the unbiased measure keeps the discriminability above zero for all scenarios. Figure 1 Keywords: Neural Dynamics*, Neural Encoding Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Naud R, Gerhard F, Mensi S and Gerstner W (2010). Improving the Discriminability of Similarity Measures for Small Sets of Spike Trains. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00102 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 31 Aug 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Richard Naud, Ecole Polytechnique Fédérale de Lausanne, Brain Mind Institute, Lausanne, Switzerland, [email protected] Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Richard Naud Felipe Gerhard Skander Mensi Wulfram Gerstner Google Richard Naud Felipe Gerhard Skander Mensi Wulfram Gerstner Google Scholar Richard Naud Felipe Gerhard Skander Mensi Wulfram Gerstner PubMed Richard Naud Felipe Gerhard Skander Mensi Wulfram Gerstner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.