Connection-type-specific biases make uniform random network models consistent with cortical recordings

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a...

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Detalles Bibliográficos
Autores principales: Tomm, Christian, Avermann, Michael, Petersen, Carl, Gerstner, Wulfram, Vogels, Tim P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Physiological Society 2014
Materias:
Neural Circuits
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4200009/
https://www.ncbi.nlm.nih.gov/pubmed/24944218
http://dx.doi.org/10.1152/jn.00629.2013

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4200009/
https://www.ncbi.nlm.nih.gov/pubmed/24944218
http://dx.doi.org/10.1152/jn.00629.2013

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