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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Detalles Bibliográficos
Autores principales: Ponghiran, Wachirawit, Srinivasan, Gopalakrishnan, Roy, Kaushik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6718696/
https://www.ncbi.nlm.nih.gov/pubmed/31507361
http://dx.doi.org/10.3389/fnins.2019.00883

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6718696/
https://www.ncbi.nlm.nih.gov/pubmed/31507361
http://dx.doi.org/10.3389/fnins.2019.00883

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