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Temporal clustering with spiking neurons and dynamic synapses: towards technological applications

Storck, J. and Jäkel, F. and Deco, G. (2001):
Temporal clustering with spiking neurons and dynamic synapses: towards technological applications.
In: Neural Networks, pp. 275-285, 14, (3), DOI: 10.1016/S0893-6080(00)00101-5,
[Online-Edition: https://doi.org/10.1016/S0893-6080(00)00101-5],
[Article]

Abstract

We apply spiking neurons with dynamic synapses to detect temporal patterns in a multi-dimensional signal. We use a network of integrate-and-fire neurons, fully connected via dynamic synapses, each of which is given by a biologically plausible dynamical model based on the exact pre- and post-synaptic spike timing. Dependent on their adaptable configuration (learning) the synapses automatically implement specific delays. Hence, each output neuron with its set of incoming synapses works as a detector for a specific temporal pattern. The whole network functions as a temporal clustering mechanism with one output per input cluster. The classification capability is demonstrated by illustrative examples including patterns from Poisson processes and the analysis of speech data.

Item Type: Article
Erschienen: 2001
Creators: Storck, J. and Jäkel, F. and Deco, G.
Title: Temporal clustering with spiking neurons and dynamic synapses: towards technological applications
Language: English
Abstract:

We apply spiking neurons with dynamic synapses to detect temporal patterns in a multi-dimensional signal. We use a network of integrate-and-fire neurons, fully connected via dynamic synapses, each of which is given by a biologically plausible dynamical model based on the exact pre- and post-synaptic spike timing. Dependent on their adaptable configuration (learning) the synapses automatically implement specific delays. Hence, each output neuron with its set of incoming synapses works as a detector for a specific temporal pattern. The whole network functions as a temporal clustering mechanism with one output per input cluster. The classification capability is demonstrated by illustrative examples including patterns from Poisson processes and the analysis of speech data.

Journal or Publication Title: Neural Networks
Volume: 14
Number: 3
Divisions: 03 Department of Human Sciences
03 Department of Human Sciences > Institute for Psychology
03 Department of Human Sciences > Institute for Psychology > Models of Higher Cognition
Date Deposited: 09 Jul 2018 09:02
DOI: 10.1016/S0893-6080(00)00101-5
Official URL: https://doi.org/10.1016/S0893-6080(00)00101-5
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