Cargando…
Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization
The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using...
Autores principales: | Wen, Haiguang, Shi, Junxing, Chen, Wei, Liu, Zhongming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830584/ https://www.ncbi.nlm.nih.gov/pubmed/29491405 http://dx.doi.org/10.1038/s41598-018-22160-9 |
Ejemplares similares
-
Key Visual Features for Rapid Categorization of Animals in Natural Scenes
por: Delorme, Arnaud, et al.
Publicado: (2010) -
Visual Cortical Entrainment to Motion and Categorical Speech Features during Silent Lipreading
por: O’Sullivan, Aisling E., et al.
Publicado: (2017) -
Categorization dynamically alters representations in human visual cortex
por: Henderson, Margaret M., et al.
Publicado: (2023) -
The Characteristics and Limits of Rapid Visual Categorization
por: Fabre-Thorpe, Michèle
Publicado: (2011) -
Correspondence of categorical and feature‐based representations of music in the human brain
por: Nakai, Tomoya, et al.
Publicado: (2020)