Reinforcement Learning With Low-Complexity Liquid State Machines

Reinforcement Learning With Low-Complexity Liquid State Machines

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 networks exhibit highly non-linear...

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Bibliographic Details
Main Authors: Wachirawit Ponghiran, Gopalakrishnan Srinivasan, Kaushik Roy
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-08-01
Series:Frontiers in Neuroscience
Subjects:
liquid state machine
recurrent SNN
learning without stable states
spiking reinforcement learning
Q-learning
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2019.00883/full

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https://www.frontiersin.org/article/10.3389/fnins.2019.00883/full

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