05961896

a

05961896 Bohte, Sander M. Error-backpropagation in networks of fractionally predictive spiking neurons. Honkela, Timo (ed.) et al., Artificial neural networks and machine learning -- ICANN 2011. 21st international conference on artificial neural networks, Espoo, Finland, June 14--17, 2011. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-21734-0/pbk). Lecture Notes in Computer Science 6791, 60-68 (2011). 2011 Berlin: Springer EN

doi:10.1007/978-3-642-21735-7_8

Summary: We develop a learning rule for networks of spiking neurons where signals are encoded using fractionally predictive spike-coding. In this paradigm, neural output signals are encoded as a sum of shifted power-law kernels. Simple greedy thresholding can compute this encoding, and spike-trains are then exactly the signal's fractional derivative. Fractionally predictive spike-coding exploits natural statistics and is consistent with observed spike-rate adaptation in real neurons; its multiple-timescale properties also reconciles notions of spike-time coding and spike-rate coding. Previously, we argued that properly tuning the decoding kernel at receiving neurons can implement spectral filtering; the applicability to general temporal filtering was left open. Here, we present an error-backpropagation algorithm to learn these decoding filters, and we show that networks of fractionally predictive spiking neurons can then implement temporal filters such as delayed responses, delayed match-to-sampling, and temporal versions of the XOR problem.