Cargando…
Neural heterogeneity promotes robust learning
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to ca...
Autores principales: | Perez-Nieves, Nicolas, Leung, Vincent C. H., Dragotti, Pier Luigi, Goodman, Dan F. M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490404/ https://www.ncbi.nlm.nih.gov/pubmed/34608134 http://dx.doi.org/10.1038/s41467-021-26022-3 |
Ejemplares similares
-
First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures
por: Liu, Siying, et al.
Publicado: (2023) -
Distributed source coding: theory, algorithms and applications
por: Dragotti, Pier Luigi, et al.
Publicado: (2009) -
Sparse sampling: theory, methods and an application in neuroscience
por: Oñativia, Jon, et al.
Publicado: (2014) -
Light-Field Microscopy for Optical Imaging of Neuronal Activity: When Model-Based Methods Meet Data-Driven Approaches
por: Song, Pingfan, et al.
Publicado: (2022) -
ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
por: Reynolds, Stephanie, et al.
Publicado: (2017)