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Title of Journal: Soft Comput

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Abbravation: Soft Computing

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Springer Berlin Heidelberg

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DOI

10.1016/0197-2456(86)90097-8

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1433-7479

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Fuzzy characterization of spike synchrony in paral

Authors: David Picado Muiño Iván Castro León Christian Borgelt
Publish Date: 2013/04/07
Volume: 18, Issue: 1, Pages: 71-83
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Abstract

We present a framework for characterizing spike and spiketrain synchrony in parallel neuronal spike trains that is based on the identification of spikes with what we call influence maps realvalued functions that describe an influence region around the corresponding spike times within which possibly graded ie fuzzy synchrony with other spikes is defined We formalize two models of synchrony in this framework the binbased model the almost exclusively applied model in the field and a novel alternative model based on a continuous graded notion of synchrony aimed at overcoming the drawbacks of the binbased model We study the task of identifying frequent and synchronous neuronal patterns from parallel spike trains in our framework formalized as an instance of what we call the fuzzy frequent pattern mining problem a generalization of standard frequent pattern mining and briefly evaluate our synchrony models on this taskThe authors would like to thank Sonja Grün from the Computational and Systems Neuroscience Department at the Institute for Neuroscience and Medicine INM6 Research Center Jülich Germany for helpful remarks made along several conversations held with her about the issues addressed in this paper

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