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ESANN
2000

SpikeProp: backpropagation for networks of spiking neurons

14 years 24 days ago
SpikeProp: backpropagation for networks of spiking neurons
Abstract. For a network of spiking neurons with reasonable postsynaptic potentials, we derive a supervised learning rule akin to traditional error-back-propagation, SpikeProp and show how to overcome the discontinuities introduced by thresholding. Using this learning algorithm, we demonstrate how networks of spiking neurons with biologically plausible time-constants can perform complex non-linear classification in fast temporal coding just as well as rate-coded networks. When comparing the (implicit) number of neurons required for the respective encodings, it is empirically demonstrated that temporal coding potentially requires significantly less neurons.
Sander M. Bohte, Joost N. Kok, Johannes A. La Pout
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where ESANN
Authors Sander M. Bohte, Joost N. Kok, Johannes A. La Poutré
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