A spiking neural network model of model-free reinforcement learning with high-dimensional sensory input and perceptual ambiguity.

ABSTRACT: A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spikin

SUBMITTER: Nakano T 

PROVIDER: S-EPMC4347982 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature