A cortical locus for the processing of contrast-defined contours

Abstract

Object boundaries in the natural environment are often defined by changes in luminance; in other cases, however, there may be no difference in average luminance across the boundary, which is instead defined by more subtle 'second-order' cues, such as changes in the contrast of a fine-grained texture. The detection of luminance boundaries may be readily explained in terms of visual cortical neurons, which compute the linear sum of the excitatory and inhibitory inputs to different parts of their receptive field. The detection of second-order stimuli is less well understood, but is thought to involve a separate nonlinear processing stream, in which boundary detectors would receive inputs from many smaller subunits. To address this, we have examined the properties of cortical neurons which respond to both first- and second-order stimuli. We show that the inputs to these neurons are also oriented, but with no fixed orientational relationship to the neurons they subserve. Our results suggest a flexible mechanism by which the visual cortex can detect object boundaries regardless of whether they are defined by luminance or texture.

Surround Suppression of V1 Neurons Mediates Orientation-Based Representation of High-Order Visual Features

“…Superficially, the stimuli used in the present study of centersurround organization in area 17 neurons and those used to study contrast-envelope-responsive neurons primarily in area 18 (Mareschal and Baker Jr 1998;Tanaka and Ohzawa 2006;Zhou and Baker Jr 1993) are very similar. Are these two phenomena-the center-surround effects and envelope responses-essentially the same and based on a common neural mechanism?…”

Neural heterogeneities influence envelope and temporal coding at the sensory periphery

“…It has been proposed that this occurs because the first order stimulus statistics are filtered linearly while the second order statistics (i.e. the contrast envelope) are encoded through a different pathway consisting of an initial linear filter, followed by nonlinear rectification, and subsequently by low-pass filtering (Mareschal and Baker, 1998). It thus appears that rectification is used in multiple sensory modalities to encode contrast modulations or envelopes.…”

“…While it is clear that envelopes are behaviourally relevant in other sensory modalities such as auditory (Smith et al, 2002;Zeng et al, 2005) and visual (Grosof et al, 1993;Mareschal and Baker, 1998;Tanaka and Ohzawa, 2006), the coding of envelopes is a relatively new subject in the electrosensory system. In wave-type weakly electric fish, envelopes of the AM are produced when fish move relative to each other and when three or more fish interact.…”

“…Psychophysical studies have shown that the information contained in the envelope waveform is sufficient for speech perception in humans (Smith et al, 2002;Zeng et al, 2005). Envelopes are also found in natural visual images and are presumably used by the brain in order to distinguish contrast-based visual contours (Grosof et al, 1993;Mareschal and Baker, 1998;Tanaka and Ohzawa, 2006). Another example of a nonlinear code consists of temporal coding where the neural response displays precision at time scales that are smaller than those contained in the stimulus waveform and has been observed in a variety of systems (Carr and Konishi, 1990;Carr, 1993;Joseph and Hyson, 1993;Johansson and Birznieks, 2004;Jones et al, 2004).…”

The cellular basis for parallel neural transmission of a high-frequency stimulus and its low-frequency envelope

“…An asymmetry in the single-neuron inputoutput transfer, such as rectification (1), will generate power at the envelope frequencies (2,3). Recent studies show that visual cortical neurons in cats respond to both low spatial frequency signals and the low-frequency spatial envelopes of high-frequency signals and also suggest that information about stimuli and their envelopes take separate and mutually exclusive linear and nonlinear pathways to reach these cortical neurons (4)(5)(6)(7)(8)(9)(10)(11). The cellular and network basis of these parallel cortical computations is not, however, understood.…”