Reinforcement learning of recurrent neural network for temporal coding

Daichi Kimura, Yoshinori Hayakawa

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)3379-3386
    Number of pages8
    Issue number16-18
    Publication statusPublished - 2008 Oct 1


    • Hodgkin-Huxley neuron
    • Order coding
    • Phase coding
    • Reinforcement learning
    • Temporal coding

    ASJC Scopus subject areas

    • Computer Science Applications
    • Cognitive Neuroscience
    • Artificial Intelligence


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