Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410762 | Neurocomputing | 2008 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Daichi Kimura, Yoshinori Hayakawa,