title: Learning optimisation by high firing irregularity.
abstract: In a network of leaky integrate-and-fire (LIF) neurons, we investigate the functional role of irregular spiking at high rates. Irregular spiking is produced by either employing the partial somatic reset mechanism on every LIF neuron of the network or by using temporally correlated inputs. In both the benchmark problem of XOR (exclusive-OR) and in a general-sum game, it is shown that irrespective of the mechanism that is used to produce it, high firing irregularity enhances the learning capability of the spiking neural network trained with reward-modulated spike-timing-dependent plasticity. These results suggest that the brain may be utilising high firing irregularity for the purposes of learning optimisation.
authors.affiliation: Department of Computer Science, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus. aris@cs.ucy.ac.cy
authors.forename: Aristodemos
authors.initials: A
authors.lastname: Cleanthous
authors.forename: Chris
authors.initials: C
authors.lastname: Christodoulou
mesh_terms: D000200:Action Potentials / Q000502:physiology; D019295:Computational Biology / Q000379:methods; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology