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A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
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"Spiking neural
networks provide a theoretically grounded means to test computational
hypotheses on neurally plausible algorithms of reinforcement learning
through numerical simulation.
...
In this work, we use a spiking neural network model to approximate the
free energy of a restricted Boltzmann machine and apply it to the
solution of PORL (partially observable reinforcement learning)
problems with high-dimensional observations.
... The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach. "
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Abstract integrate-and-fire leaky neuron Show
Other
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Nakano T, Otsuka M, Yoshimoto J, Doya K (2015) Show
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Nakano, Takashi [nakano.takashi at gmail.com] Show
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nakano.takashi@gmail.com
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