Second order dimensionality reduction using minimum and maximum mutual information models.
Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To ov...
Guardado en:
Autores principales: | Jeffrey D Fitzgerald, Ryan J Rowekamp, Lawrence C Sincich, Tatyana O Sharpee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2011
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3d31e147728e467f9687e6bc3f6a6e5c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Cross-orientation suppression in visual area V2
por: Ryan J. Rowekamp, et al.
Publicado: (2017) -
Bias reduction of a conditional maximum likelihood estimator for a Gaussian second-order moving average model
por: Fumiaki Honda, et al.
Publicado: (2021) -
On the maximum number of period annuli for second order conservative equations
por: Armands Gritsans, et al.
Publicado: (2021) -
Teaching and learning process for mathematization activities: The case of solving maximum and minimum problems
por: Al Jupri, et al.
Publicado: (2021) -
Examining the prey mass of terrestrial and aquatic carnivorous mammals: minimum, maximum and range.
por: Marlee A Tucker, et al.
Publicado: (2014)