Cargando…
A solution to the learning dilemma for recurrent networks of spiking neurons
Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this p...
Autores principales: | Bellec, Guillaume, Scherr, Franz, Subramoney, Anand, Hajek, Elias, Salaj, Darjan, Legenstein, Robert, Maass, Wolfgang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367848/ https://www.ncbi.nlm.nih.gov/pubmed/32681001 http://dx.doi.org/10.1038/s41467-020-17236-y |
Ejemplares similares
-
Spike frequency adaptation supports network computations on temporally dispersed information
por: Salaj, Darjan, et al.
Publicado: (2021) -
Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment
por: Legenstein, Robert, et al.
Publicado: (2014) -
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
por: Bill, Johannes, et al.
Publicado: (2015) -
A Learning Theory for Reward-Modulated Spike-Timing-Dependent
Plasticity with Application to Biofeedback
por: Legenstein, Robert, et al.
Publicado: (2008) -
STDP Forms Associations between Memory Traces in Networks of Spiking Neurons
por: Pokorny, Christoph, et al.
Publicado: (2020)