Cargando…
HOW CONNECTIVITY STRUCTURE SHAPES RICH AND LAZY LEARNING IN NEURAL CIRCUITS
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) chan...
Autores principales: | Liu, Yuhan Helena, Baratin, Aristide, Cornford, Jonathan, Mihalas, Stefan, Shea-Brown, Eric, Lajoie, Guillaume |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593070/ https://www.ncbi.nlm.nih.gov/pubmed/37873007 |
Ejemplares similares
-
Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
por: Liu, Yuhan Helena, et al.
Publicado: (2021) -
Structured chaos shapes spike-response noise entropy in balanced neural networks
por: Lajoie, Guillaume, et al.
Publicado: (2014) -
Free Food and Laziness
por: Carlile, W.
Publicado: (1907) -
Lazy hazy days
por: Jacobs, Howy
Publicado: (2022) -
LAZY3 interacts with LAZY2 to regulate tiller angle by modulating shoot gravity perception in rice
por: Cai, Yueyue, et al.
Publicado: (2023)