Reinforcement Learning With Low-Complexity Liquid State Machines
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DOI: 10.3389/fnins.2019.00883
Abstract: We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent spiking… read more here.
Keywords: state; complexity liquid; trainable parameters; learning low ... See more keywords