BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neuro...

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Detalles Bibliográficos
Autores principales: Gao, Yuyang, Ascoli, Giorgio A., Zhao, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203915/
https://www.ncbi.nlm.nih.gov/pubmed/34140886
http://dx.doi.org/10.3389/fnbot.2021.567482