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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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| Publicado en: | Nat Commun |
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| Autores principales: | , , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Nature Publishing Group UK
2020
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7275091/ https://ncbi.nlm.nih.gov/pubmed/32503982 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-16548-3 |
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