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A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
nakanoEtAl2015 [124669]
"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. "
  • Abstract integrate-and-fire leaky neuron Show Other
  • Nakano T, Otsuka M, Yoshimoto J, Doya K (2015) Show Other
  • Nakano, Takashi [nakano.takashi at gmail.com] Show Other
nakano.takashi@gmail.com
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Revisions: 5
Last Time: 3/9/2015 10:51:41 AM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin