Cargando…
Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems
Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggest...
Autores principales: | Lajoie, Guillaume, Lin, Kevin K., Thivierge, Jean-Philippe, Shea-Brown, Eric |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156368/ https://www.ncbi.nlm.nih.gov/pubmed/27973557 http://dx.doi.org/10.1371/journal.pcbi.1005258 |
Ejemplares similares
-
Structured chaos shapes joint spike-response noise entropy in temporally driven balanced networks
por: Lajoie, Guillaume, et al.
Publicado: (2014) -
Structured chaos shapes spike-response noise entropy in balanced neural networks
por: Lajoie, Guillaume, et al.
Publicado: (2014) -
Extreme sensitivity of reservoir computing to small network disruptions
por: Vincent-Lamarre, Philippe, et al.
Publicado: (2015) -
Decoherence, determinism and chaos revisited
por: Noyes, H P
Publicado: (1994) -
Noise- and stimulus-dependence of the optimal encoding nonlinearities in a simple ON/OFF retinal circuit model
por: Brinkman, Braden A W, et al.
Publicado: (2014)