Cargando…
Communication Sparsity in Distributed Spiking Neural Network Simulations to Improve Scalability
In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) simulations to aid discovery and experimentation. Such an approach increases the computational dem...
Autores principales: | Fernandez-Musoles, Carlos, Coca, Daniel, Richmond, Paul |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454199/ https://www.ncbi.nlm.nih.gov/pubmed/31001102 http://dx.doi.org/10.3389/fninf.2019.00019 |
Ejemplares similares
-
Backpropagation With Sparsity Regularization for Spiking Neural Network Learning
por: Yan, Yulong, et al.
Publicado: (2022) -
Efficiently passing messages in distributed spiking neural network simulation
por: Thibeault, Corey M., et al.
Publicado: (2013) -
Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions
por: Jordan, Jakob, et al.
Publicado: (2020) -
A scalable implementation of the recursive least-squares algorithm for training spiking neural networks
por: Arthur, Benjamin J., et al.
Publicado: (2023) -
Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers
por: Jordan, Jakob, et al.
Publicado: (2018)