Reducing the computational footprint for real-time BCPNN learning

The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagat...

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Detalles Bibliográficos
Autores principales: Vogginger, Bernhard, Schüffny, René, Lansner, Anders, Cederström, Love, Partzsch, Johannes, Höppner, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302947/
https://www.ncbi.nlm.nih.gov/pubmed/25657618
http://dx.doi.org/10.3389/fnins.2015.00002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302947/
https://www.ncbi.nlm.nih.gov/pubmed/25657618
http://dx.doi.org/10.3389/fnins.2015.00002

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