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 multil...

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Bibliographic Details
Published in:Neural networks 2004-06, Vol.17 (5), p.663-679
Main Authors: Prenger, Ryan, Wu, Michael C.-K., David, Stephen V., Gallant, Jack L.
Format: Article
Language:English
Subjects:
Action Potentials
Algorithms
Animals
Computer Simulation
Feedback - physiology
Humans
Models, Neurological
Multi-layer perceptron
Natural vision
Neural Networks (Computer)
Neurons - physiology
Nonparametric model
Orientation
Photic Stimulation - methods
Prediction
Reaction Time
Receptive field
Reverse correlation
Space Perception - physiology
Striate cortex
Visual Cortex - cytology
Visual Fields
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2004.03.008