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Accounting
for Surround Suppression in V1 Neurons Using a Statistically Optimized
Normalization Model. O Schwartz and E P Simoncelli. ARVO, May 1999.
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Purpose: A number of authors have used normalization models to successfully
fit steady-state response data of V1 simple cells. Rather than adjusting
model parameters to fit such data, we have developed a normalization
model whose parameters are fully specified by the statistics of an ensemble
of natural images (Simoncelli & Schwartz, ARVO-98). We show that
this model can account for suppression of V1 responses by stimuli presented
in an annular region surrounding the classical receptive field. Methods:
The stimulus is decomposed using a fixed set of linear receptive fields
at different scales, orientations, and spatial positions. A model neuron's
response is computed by squaring the linear response and dividing by
the weighted sum of squared linear responses of neighboring neurons
and an additive constant. Both the normalization weights and the constant
are optimized to maximize the statistical independence of responses
over an ensemble of natural images. In addition, we examine the variability
in model neuron responses when these parameters are optimized for individual
images. Results: The simulations are consistent with electro-physiological
data obtained in two laboratories (Cavanaugh et al. 1998, Müller
at al. 1998). In particular, the model responses match the steady state
responses of the neuron as a function of orientation, spatial frequency
and proximity of the surround. Moreover, the variability of suppression
strength when the model parameters are optimized for individual images
is no greater than the variability of the physiological measurements
across a population of neurons. Conclusions: A weighted normalization
model, in which all parameters are derived from the statistics of an
ensemble of natural images, can account for a variety of surround suppression
effects, consistent with the hypothesis that visual neural computations
are matched to the statistics of natural images.