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...
Saved in:
Main Authors: | Ziniu Wu, Harold Rockwell, Yimeng Zhang, Shiming Tang, Tai Sing Lee |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/dce80bd5117545c39b83f2276e7d9353 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons.
by: Ziniu Wu, et al.
Published: (2021) -
Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input.
by: Jonathan J Hunt, et al.
Published: (2013) -
Spatiotemporal functional organization of excitatory synaptic inputs onto macaque V1 neurons
by: Niansheng Ju, et al.
Published: (2020) -
Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4
by: Mehdi Sanayei, et al.
Published: (2018) -
Invariance of visual operations at the level of receptive fields.
by: Tony Lindeberg
Published: (2013)