publication . Article . Other literature type . 2013

Training spiking neural networks to associate spatio-temporal input-output spike patterns

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N;
Open Access English
  • Published: 01 Jan 2013
  • Publisher: Elsevier
  • Country: Switzerland
Abstract
In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the proposed learning rule. Furthermore, we extend the application of the algorithm to train a SNN consisting of multiple spiking neurons to perform spatiotemporal pattern classific...
Subjects
arXiv: Quantitative Biology::Neurons and CognitionComputer Science::Emerging Technologies
ACM Computing Classification System: ComputerSystemsOrganization_PROCESSORARCHITECTURES
free text keywords: Institute of Neuroinformatics, 570 Life sciences; biology
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