Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons

Peter Tino, A Mills

Research output: Contribution to journalArticle

23 Citations (Scopus)


We investigate possibilities of inducing temporal structures without fading memory in recurrent networks of spiking neurons strictly operating in the pulse-coding regime. We extend the existing gradient-based algorithm for training feedforward spiking neuron networks, SpikeProp (Bohte, Kok, & La Poutré, 2002), to recurrent network topologies, so that temporal dependencies in the input stream are taken into account. It is shown that temporal structures with unbounded input memory specified by simple Moore machines (MM) can be induced by recurrent spiking neuron networks (RSNN). The networks are able to discover pulse-coded representations of abstract information processing states coding potentially unbounded histories of processed inputs. We show that it is often possible to extract from trained RSNN the target MM by grouping together similar spike trains appearing in the recurrent layer. Even when the target MM was not perfectly induced in a RSNN, the extraction procedure was able to reveal weaknesses of the induced mechanism and the extent to which the target machine had been learned.
Original languageEnglish
Pages (from-to)591-613
Number of pages23
JournalNeural Computation
Issue number3
Publication statusPublished - 1 Mar 2006


Dive into the research topics of 'Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons'. Together they form a unique fingerprint.

Cite this