Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks.

ABSTRACT: Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified ad hoc by the researcher. This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Additionally, this approach presupposes knowledge on the part of the researcher on how the environment should be partitioned and represented and scales poorly with the size or complexity of the environment. To add

SUBMITTER: Weidel P 

PROVIDER: S-EPMC7970044 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature