Publication: Training spiking neural networks to associate spatio-temporal input-output spike patterns
Training spiking neural networks to associate spatio-temporal input-output spike patterns
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Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N. (2013). Training spiking neural networks to associate spatio-temporal input-output spike patterns. Neurocomputing, 107, 3–10. https://doi.org/10.1016/j.neucom.2012.08.034
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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
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- Mohemmed, A
- Schliebs, S
- Matsuda, S
- Kasabov, N
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Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N. (2013). Training spiking neural networks to associate spatio-temporal input-output spike patterns. Neurocomputing, 107, 3–10. https://doi.org/10.1016/j.neucom.2012.08.034
