Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification

Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships...

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Detalles Bibliográficos
Autores principales: Theis, Lucas, Chagas, Andrè Maia, Arnstein, Daniel, Schwarz, Cornelius, Bethge, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836720/
https://www.ncbi.nlm.nih.gov/pubmed/24278006
http://dx.doi.org/10.1371/journal.pcbi.1003356

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836720/
https://www.ncbi.nlm.nih.gov/pubmed/24278006
http://dx.doi.org/10.1371/journal.pcbi.1003356

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