Cargando…
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other v...
Autores principales: | Nessler, Bernhard, Pfeiffer, Michael, Buesing, Lars, Maass, Wolfgang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3636028/ https://www.ncbi.nlm.nih.gov/pubmed/23633941 http://dx.doi.org/10.1371/journal.pcbi.1003037 |
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) -
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons
por: Buesing, Lars, et al.
Publicado: (2011) -
Stochastic Computations in Cortical Microcircuit Models
por: Habenschuss, Stefan, et al.
Publicado: (2013) -
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons
por: Pecevski, Dejan, et al.
Publicado: (2011) -
Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses
por: Ocker, Gabriel Koch, et al.
Publicado: (2015)