Temporal pattern identification using spike-timing dependent plasticity

Abstract

This paper addresses the question of the functional role of the dual application of positive and negative Hebbian time dependent plasticity rules, in the particular framework of reinforcement learning tasks. Our simulations take place in a recurrent network of spiking neurons with inhomogeneous synaptic weights. A spike-timing dependent plasticity (STDP) rule is combined with its “opposite”, the “anti-STDP”. A local regulation mechanism moreover maintains the postsynaptic neuron in the vicinity of a reference frequency, which forces the global dynamics to be maintained in a softly disordered regime. This approach is tested on a simple discrimination task which requires short-term memory: temporal pattern classification. We show that such temporal patterns can be categorised, and present tracks for future improvements.