Embedded neural controllers based on spiking neuron models

Bakó, László and Brassai, Sándor (2009) Embedded neural controllers based on spiking neuron models. Pollack Periodica, 4 (3). pp. 143-154. ISSN 1788-1994

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This paper demonstrates, that input patterns can be encoded in the synaptic weights by local Hebbian delay-learning of spiking neurons (SN), where, after learning, the firing time of an output neuron reflects the distance of the evaluated pattern to its learned input pattern thus realizing a kind of RBF behavior. Furthermore, the paper shows, that temporal spike-time coding and Hebbian learning is a viable means for unsupervised computation in a network of SNs, as the network is capable of clustering realistic data. Then, two versions — with and without embedded micro-controllers — of a SNN are implemented for the aforementioned task.

Item Type: Article
Subjects: T Technology / alkalmazott, műszaki tudományok > TA Engineering (General). Civil engineering (General) / általános mérnöki tudományok
Depositing User: Erika Bilicsi
Date Deposited: 04 Nov 2017 09:50
Last Modified: 04 Nov 2017 09:50

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