The bifurcating neuron network 1
Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The bifurcating neuron network 2: an analog associative memory
Neural Networks
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
A Hebbian-based reinforcement learning framework for spike-timing-dependent synapses
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
A reinforcement learning framework for spiking networks with dynamic synapses
Computational Intelligence and Neuroscience
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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.