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. "
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]