Reinforcement learning of recurrent neural network for temporal coding
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 HodgkinHuxley 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.
reinforcement learning temporal coding hodgkinhuxley neuron order coding phase coding