Research ReportLearning optimisation by high firing irregularity

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.

► We show, using a computational model, that irregular firing at high rates has the functional role of learning optimisation. ► This is tested on the XOR problem and the Iterated Prisoner's Dilemma. ► Irregular spiking is either produced by partial somatic reset on the network's units or by using temporally correlated inputs. ► The efficiency of with reward-modulated spike-timing-dependent plasticity is enhanced on both learning tasks.