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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83992

Jäkel,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Storck, J., Jäkel, F., & Deco, G. (2001). Temporal clustering with spiking neurons and dynamic synapses: towards technological applications. Neural Networks, 14(3), 275-285. doi:10.1016/S0893-6080(00)00101-5.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E2A6-3
Zusammenfassung
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.