Control of criticality and computation in spiking neuromorphic networks with plasticity

The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying com...

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Detalles Bibliográficos
Autores principales: Cramer, Benjamin, Stöckel, David, Kreft, Markus, Wibral, Michael, Schemmel, Johannes, Meier, Karlheinz, Priesemann, Viola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275091/
https://www.ncbi.nlm.nih.gov/pubmed/32503982
http://dx.doi.org/10.1038/s41467-020-16548-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275091/
https://www.ncbi.nlm.nih.gov/pubmed/32503982
http://dx.doi.org/10.1038/s41467-020-16548-3

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