Accounting for surround suppression in V1 neurons using a statistically optimized normalization model

O Schwartz and E P Simoncelli

Published in Investigative Opthalmology and Visual Science Supplement (ARVO), vol.40 pp. S-641, May 1999.

This paper has been superseded by:
Natural signal statistics and sensory gain control
O Schwartz and E P Simoncelli.
Nature Neuroscience, vol.4(8), pp. 819--825, Aug 2001 .

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.

Warning: Undefined array key 2 in /System/Volumes/Data/e/1.3/p1/lcv/html_public/pubs/makeAbs.php on line 304

Warning: Undefined array key 2 in /System/Volumes/Data/e/1.3/p1/lcv/html_public/pubs/makeAbs.php on line 304
  • nips*98: Simoncelli98d
  • Nature Neuroscience article: Schwartz01
  • Listing of all publications