Abstract
Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations-local oriented filters and divisive normalization-whereas the second stage involves novel computations-compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.
| Original language | English (US) |
|---|---|
| Article number | e1003079 |
| Journal | PLoS Computational Biology |
| Volume | 9 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2013 |
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ASJC Scopus subject areas
- Cellular and Molecular Neuroscience
- Ecology
- Molecular Biology
- Genetics
- Ecology, Evolution, Behavior and Systematics
- Modeling and Simulation
- Computational Theory and Mathematics
Cite this
A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex. / Kay, Kendrick N.; Winawer, Jonathan; Rokem, Ariel; Mezer, Aviv; Wandell, Brian A.
In: PLoS Computational Biology, Vol. 9, No. 5, e1003079, 05.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex
AU - Kay, Kendrick N.
AU - Winawer, Jonathan
AU - Rokem, Ariel
AU - Mezer, Aviv
AU - Wandell, Brian A.
PY - 2013/5
Y1 - 2013/5
N2 - Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations-local oriented filters and divisive normalization-whereas the second stage involves novel computations-compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.
AB - Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations-local oriented filters and divisive normalization-whereas the second stage involves novel computations-compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.
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U2 - 10.1371/journal.pcbi.1003079
DO - 10.1371/journal.pcbi.1003079
M3 - Article
C2 - 23737741
AN - SCOPUS:84878495139
VL - 9
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 5
M1 - e1003079
ER -