Cargando…
Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator
One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used. However, such hardware can be costly, not readily available, or require specialized software implementations that are difficul...
Autores principales: | Gosmann, Jan, Eliasmith, Chris |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415674/ https://www.ncbi.nlm.nih.gov/pubmed/28522970 http://dx.doi.org/10.3389/fninf.2017.00033 |
Ejemplares similares
-
Python Scripting in the Nengo Simulator
por: Stewart, Terrence C., et al.
Publicado: (2009) -
Benchmarking neuromorphic systems with Nengo
por: Bekolay, Trevor, et al.
Publicado: (2015) -
Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics
por: DeWolf, Travis, et al.
Publicado: (2020) -
Nengo: a Python tool for building large-scale functional brain models
por: Bekolay, Trevor, et al.
Publicado: (2014) -
Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks
por: Gosmann, Jan, et al.
Publicado: (2016)