Cargando…
Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are ofte...
Autores principales: | Wu, Ziniu, Rockwell, Harold, Zhang, Yimeng, Tang, Shiming, Lee, Tai Sing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589190/ https://www.ncbi.nlm.nih.gov/pubmed/34695120 http://dx.doi.org/10.1371/journal.pcbi.1009528 |
Ejemplares similares
-
Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus
por: Carlson, Nicole L., et al.
Publicado: (2012) -
Sparse coding models demonstrate some non-classical receptive field effects
por: Zhu, Mengchen, et al.
Publicado: (2010) -
A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields
por: Zylberberg, Joel, et al.
Publicado: (2011) -
Large-scale two-photon imaging revealed super-sparse population codes in the V1 superficial layer of awake monkeys
por: Tang, Shiming, et al.
Publicado: (2018) -
Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System
por: Zhu, Mengchen, et al.
Publicado: (2013)