Cargando…
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons
The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there ex...
Autores principales: | Buesing, Lars, Bill, Johannes, Nessler, Bernhard, Maass, Wolfgang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207943/ https://www.ncbi.nlm.nih.gov/pubmed/22096452 http://dx.doi.org/10.1371/journal.pcbi.1002211 |
Ejemplares similares
-
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
por: Bill, Johannes, et al.
Publicado: (2015) -
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons
por: Pecevski, Dejan, et al.
Publicado: (2011) -
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
por: Nessler, Bernhard, et al.
Publicado: (2013) -
Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
por: Zhang, Wen-Hao, et al.
Publicado: (2023) -
Multitask computation through dynamics in recurrent spiking neural networks
por: Pugavko, Mechislav M., et al.
Publicado: (2023)