A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)

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Accession:168143
"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. "
Reference:
1 . Nakano T, Otsuka M, Yoshimoto J, Doya K (2015) A spiking neural network model of model-free reinforcement learning with high-dimensional sensory input and perceptual ambiguity. PLoS One 10:e0115620 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEST;
Model Concept(s): Reinforcement Learning;
Implementer(s): Nakano, Takashi [nakano.takashi at gmail.com];
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