Storck, J. ; Jäkel, F. ; Deco, G. (2001)
Temporal clustering with spiking neurons and dynamic synapses: towards technological applications.
In: Neural Networks, 14 (3)
doi: 10.1016/S0893-6080(00)00101-5
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2001 |
| Autor(en): | Storck, J. ; Jäkel, F. ; Deco, G. |
| Art des Eintrags: | Bibliographie |
| Titel: | Temporal clustering with spiking neurons and dynamic synapses: towards technological applications |
| Sprache: | Englisch |
| Publikationsjahr: | 2001 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Neural Networks |
| Jahrgang/Volume einer Zeitschrift: | 14 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.1016/S0893-6080(00)00101-5 |
| URL / URN: | https://doi.org/10.1016/S0893-6080(00)00101-5 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition |
| Hinterlegungsdatum: | 09 Jul 2018 09:02 |
| Letzte Änderung: | 12 Okt 2020 11:43 |
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