Title
Signal-Tuned Gabor Functions as Models for Stimulus-Dependent Cortical Receptive Fields
Abstract
We propose and analyze a model, based on signal-tuned Gabor functions, for the receptive fields and responses of V1 cells. Signal-tuned Gabor functions are gaussian-modulated sinusoids whose parameters are obtained from a given, spatial, or spectral “tuning” signal. These functions can be proven to yield exact representations of their tuning signals and have recently been proposed as the kernels of a variant Gabor transform—the signal-tuned Gabor transform (STGT)—which allows the accurate detection of spatial and spectral events. Here we show that by modeling the receptive fields of simple and complex cells as signal-tuned Gabor functions and expressing their responses as STGTs, we are able to replicate the properties of these cells when tested with standard grating and slit inputs, at the same time emulating their stimulus-dependent character as revealed by recent neurophysiological studies.
Year
DOI
Venue
2014
10.1162/NECO_a_00581
Neural Computation  
Field
DocType
Volume
Receptive field,Computer vision,Grating,Neurophysiology,Gabor wavelet,Artificial intelligence,Stimulus (physiology),Gabor transform,Machine learning,Mathematics
Journal
26
Issue
ISSN
Citations 
5
0899-7667
1
PageRank 
References 
Authors
0.63
6
3
Name
Order
Citations
PageRank
José R. A. Torreão15910.18
Silvia M. C. Victer241.60
Marcos S. Amaral310.63