Cargando…
A Low-Rank Method for Characterizing High-Level Neural Computations
The signal transformations that take place in high-level sensory regions of the brain remain enigmatic because of the many nonlinear transformations that separate responses of these neurons from the input stimuli. One would like to have dimensionality reduction methods that can describe responses of...
Autores principales: | Kaardal, Joel T., Theunissen, Frédéric E., Sharpee, Tatyana O. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534486/ https://www.ncbi.nlm.nih.gov/pubmed/28824408 http://dx.doi.org/10.3389/fncom.2017.00068 |
Ejemplares similares
-
How Invariant Feature Selectivity Is Achieved in Cortex
por: Sharpee, Tatyana O.
Publicado: (2016) -
Editorial: Advances in Computational Neuroscience
por: Nowotny, Thomas, et al.
Publicado: (2022) -
Maximally informative foraging by Caenorhabditis elegans
por: Calhoun, Adam J, et al.
Publicado: (2014) -
Analyzing multicomponent receptive fields from neural responses to natural stimuli
por: Rowekamp, Ryan, et al.
Publicado: (2011) -
Neural Decision Boundaries for Maximal Information Transmission
por: Sharpee, Tatyana, et al.
Publicado: (2007)