Automatic learning of synchrony in neuronal electrode recordings

DOI

10.2991/ifsa-eusflat-15.2015.174How to use a DOI?

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

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - David Picado-Muiño
AU  - Christian Borgelt
PY  - 2015/06
DA  - 2015/06
TI  - Automatic learning of synchrony in neuronal electrode recordings
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1231
EP  - 1237
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.174
DO  - 10.2991/ifsa-eusflat-15.2015.174
ID  - Picado-Muiño2015/06
ER  -