Nonlinear V1 responses to natural scenes revealed by neural network analysis.
A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes.
Department of Physics, University of California Berkeley, Berkeley CA, USA.
Ryan
R
Prenger
Michael C-K
MC
Wu
Stephen V
SV
David
Jack L
JL
Gallant
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009949:Orientation; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time; D013028:Space Perception / Q000502:physiology*; D014793:Visual Cortex / Q000166:cytology*; D014794:Visual Fields