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Biologically-inspired neuronal adaptation improves learning in neural networks
Since humans still outperform artificial neural networks on many tasks, drawing inspiration from the brain may help to improve current machine learning algorithms. Contrastive Hebbian learning (CHL) and equilibrium propagation (EP) are biologically plausible algorithms that update weights using only...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Taylor & Francis
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851208/ https://www.ncbi.nlm.nih.gov/pubmed/36685291 http://dx.doi.org/10.1080/19420889.2022.2163131 |