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Learning probabilistic neural representations with randomly connected circuits
The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood o...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
National Academy of Sciences
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547210/ https://www.ncbi.nlm.nih.gov/pubmed/32948691 http://dx.doi.org/10.1073/pnas.1912804117 |