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Synfire graphs: from spike patterns to automata of spiking neurons

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39 S.
Veröffentlichung
2013-06-19
Authors
Wennekers, Thomas
Bericht


Faculties
Fakultät für Ingenieurwissenschaften und Informatik
Series
Ulmer Informatik-Berichte
Abstract
The concept of synfire chains has been proposed by Abeles as a reason-able biophysical model for cortical long-time correlations and replicating spike patterns in multi unit recordings. Some recent computational modelling approaches extend the model into a functional direction proposing that the synchronization of synfire chains may help to solve the binding problem of cortical information processing. In the present paper we investigate further computational aspects of synfire chains. First, we show how they can be used as spatio-temporal feature stores capable to learn, regenerate and recognize spatio-temporal signals. Thereby synfire chains introduce time into the static world of attractor neural networks as paradigms for cortical information processing. Then we extend the synfire chain model from linear autonomously evolving networks to graph-like structures with external input signals. Such synfire graphs can implement arbitrary deterministic and nondeterministic finite state automata. We prove formally that synfire graphs consisting of time-continuous spiking neurons can robustly process arbitrary long input words even if realistic postsynaptic potentials, bounded background noise and spike-timing jitter are taken into consideration. A single synfire node may consist of a single spiking neuron or a larger set of cells. In the latter case connections between two nodes can be diluted or have otherwise random synaptic efficacies. The extension of synfire chains to synfire graphs introduces operational (logical, procedural, cognitive) components into common modelling of Hebbian cell assemblies and brain functioning.
Date created
1998
Subject headings
[GND]: Endlicher Automat | Hirnforschung
[LCSH]: Neural networks (Computer science)
[DDC subject group]: DDC 004 / Data processing & computer science
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https://oparu.uni-ulm.de/xmlui/license_v3

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DOI & citation

Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-2497

Wennekers, Thomas (2013): Synfire graphs: from spike patterns to automata of spiking neurons. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-2497
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