title:
 
Automatic learning of synchrony in neuronal electrode recordings
publication:
 
eusflat-15
ISBN:
  978-94-62520-77-6
ISSN:
  1951-6851
DOI:
  doi:10.2991/ifsa-eusflat-15.2015.174 (how to use a DOI)
author(s):
 
David Picado-Muiņo, Christian Borgelt
corresponding author:
 
David Picado-Muiņo
publication date:
 
June 2015
keywords:
 
Synchronous spiking, parallel spike trains, parallel point processes, multiple electrode recordings, synchrony in spike-train databases, automatic learning.
abstract:
 
Synchrony among neuronal impulses (or spikes) plays, according to some of the most prominent neural coding hypotheses, a central role in information processing in biological neural networks. When dealing with multiple electrode recordings (i.e., spike trains) modelers generally characterize synchrony by means of a maximal time span (since exact spike-time coincidences cannot be expected): two or more spikes are regarded as synchronous if they lie from each other within a distance at most this maximal time span. Such time span is determined by the modeler and there is no agreement about how long it should be. In this paper we present methodology to learn this time span automatically from spike-train data that involves the assessment of the amount of synchrony in the database (relative to that expected if spike trains in it were uncorrelated) and a learning process that looks at the time span that maximizes it (over all those considered).
copyright:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: