Publications
] .
2020. Evidence for the intrinsically nonlinear nature of receptive fields in vision. Scientific reports. 
NLreceptiveFields.pdf (2.65 MB)
NLreceptiveFields.pdf (2.65 MB).
2020. Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications. Vision Research. visualIllusions.pdf (3.73 MB)
visualIllusions.pdf (3.73 MB).
2020. Visual information flow in Wilson-Cowan networks. Journal of Neurophysiology. VisualInformation.pdf (3.12 MB)
VisualInformation.pdf (3.12 MB).
2019. In Praise of Artifice Reloaded: Caution With Natural Image Databases in Modeling Vision. *Accepted* in Frontiers in Neuroscience. NatImagesDatabases.pdf (4.25 MB)
NatImagesDatabases.pdf (4.25 MB).
2017. Derivatives and Inverse of Cascaded Linear+Nonlinear Neural Models. PLOS ONE. paper.pdf (7.86 MB)
paper.pdf (7.86 MB).
2017. The Wilson-Cowan Model Describes Contrast Response and Subjective Distortion. Vision Sciences Society Annual Meeting. VSS_2017_M.pdf (384.41 KB)
VSS_2017_M.pdf (384.41 KB)