Reinforcement learning of recurrent neural network for temporal coding

Abstract

We study a reinforcement learning for temporal coding with neural network consisting of stochastic spiking neurons. In neural networks, information can be coded by characteristics of the timing of each neuronal firing, including the order of firing or the relative phase differences of firing. We derive the learning rule for this network and show that the network consisting of Hodgkin-Huxley neurons with the dynamical synaptic kinetics can learn the appropriate timing of each neuronal firing. We also investigate the system size dependence of learning efficiency.

17 pages, 6 figures

References in corpus (4)

• Fast Response and Temporal Coding on Coherent Oscillations in Small-World Networks
• Gradient learning in spiking neural networks by dynamic perturbation of conductances
• Fastest learning in small world neural networks
• An associative memory of Hodgkin-Huxley neuron networks with Willshaw-type synaptic couplings