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Sequence learning with hidden units in spiking neural networks
Johanni Brea · Walter Senn · Jean-Pascal Pfister

Tue Dec 13 03:44 AM -- 03:48 AM (PST) @ None

We consider a statistical framework in which recurrent networks of spiking neurons learn to generate spatio-temporal spike patterns. Given biologically realistic stochastic neuronal dynamics we derive a tractable learning rule for the synaptic weights towards hidden and visible neurons that leads to optimal recall of the training sequences. We show that learning synaptic weights towards hidden neurons significantly improves the storing capacity of the network. Furthermore, we derive an approximate online learning rule and show that our learning rule is consistent with Spike-Timing Dependent Plasticity in that if a presynaptic spike shortly precedes a postynaptic spike, potentiation is induced and otherwise depression is elicited.

Author Information

Johanni Brea (Universität Bern)
Walter Senn (University of Bern)
Jean-Pascal Pfister (Cambridge University)

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