Cargando…
Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder...
Autores principales: | Frenkel, Charlotte, Lefebvre, Martin, Bol, David |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902857/ https://www.ncbi.nlm.nih.gov/pubmed/33642986 http://dx.doi.org/10.3389/fnins.2021.629892 |
Ejemplares similares
-
Notes on Visual Cortical Feedback and Feedforward Connections
por: Rockland, Kathleen S.
Publicado: (2022) -
Enhanced HMAX model with feedforward feature learning for multiclass categorization
por: Li, Yinlin, et al.
Publicado: (2015) -
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
por: Moldovan, Adrian, et al.
Publicado: (2020) -
Short-Term Sensorimotor Deprivation Impacts Feedforward and Feedback Processes of Motor Control
por: Scotto, Cécile R., et al.
Publicado: (2020) -
Neural dynamics of feedforward and feedback processing in figure-ground segregation
por: Layton, Oliver W., et al.
Publicado: (2014)