Local non-linear interactions in the visual cortex may reflect global decorrelation

Abstract

The classical receptive field in the primary visual cortex have been successfully explained by sparse activation of relatively independent units, whose tuning properties reflect the statistical dependencies in the natural environment. Robust surround modulation, emerging from stimulation beyond the classical receptive field, has been associated with increase of lifetime sparseness in the V1, but the system-wide modulation of response strength have currently no theoretical explanation. We measured fMRI responses from human visual cortex and quantified the contextual modulation with a decorrelation coefficient (d), derived from a subtractive normalization model. All active cortical areas demonstrated local non-linear summation of responses, which were in line with hypothesis of global decorrelation of voxels responses. In addition, we found sensitivity to surrounding stimulus structure across the ventral stream, and large-scale sensitivity to the number of simultaneous objects. Response sparseness across voxel population increased consistently with larger stimuli. These data suggest that contextual modulation for a stimulus event reflect optimization of the code and perhaps increase in energy efficiency throughout the ventral stream hierarchy. Our model provides a novel prediction that average suppression of response amplitude for simultaneous stimuli across the cortical network is a monotonic function of similarity of response strengths in the network when the stimuli are presented alone.

Compressive spatial summation in human visual cortex

“…Simultaneous presentation engages subadditive spatial summation, whereas sequential presentation does not. Finally, our summation results are consistent with recent BOLD measurements of nonlinear spatial effects throughout visual cortex (Vanni and Rosenstrom 2011). This study also found increasingly nonlinear effects in anterior visual areas and proposed an alternative, informationtheoretic explanation for the findings.…”

“…A pRF is the region of the visual field within which stimuli evoke responses from a local population of neurons (Dumoulin and Wandell 2008;Victor et al 1994). Existing models of pRFs use linear weights applied to the stimulus contrast (Dumoulin and Wandell 2008; Larsson and Heeger 2006;Thirion et al 2006) and therefore predict linear, not subadditive, summation.…”

Multiple components of surround modulation in primary visual cortex: Multiple neural circuits with multiple functions?

“…Vinje & Gallant (2000) recorded the responses of V1 cells to natural images, and found that the responses of separately recorded neuron pairs were less correlated when both the RF and surround were simultaneously stimulated, compared to when only the RF was stimulated. Similarly, a previous fMRI study (Vanni & Rosenström, 2011) showed that responses to complete human faces could be predicted by assuming that the responses to face parts were decorrelated in the brain. These studies suggest that, by decreasing dependencies between neuronal responses, surround modulation increases the amount of information that a neural population conveys about natural images.…”

“…While we recognize that this similarity, as presented here, is qualitative, it supports our hypothesis that surround modulation reflects the statistical dependencies in the natural visual environment. A number of previous studies have also related surround modulation and neuronal dependencies in V1 (Coen-Cagli, Dayan, & Schwartz, 2012; Schwartz & Simoncelli, 2001; Shushruth et al, 2013; Vanni & Rosenström, 2011; Vinje & Gallant, 2000). For example, Schwartz and Simoncelli (2001) showed that properties such as the orientation tuning of surround modulation and the contrast dependence of size tuning arise from a divisive normalization model in which the model parameters are optimized to maximize the statistical independencies between the responses of the model neurons.…”

Information theory in neuroscience

“…The overwhelming majority of studies concern the implications of this principle for processing at a neuronal level (e.g., Atick and Redlich 1990). In this issue, however, Vanni and Rosenström (2011) consider a larger scale of organization. Through the use of functional imaging techniques to assess the distribution of activity levels in neuronal populations in visual cortex, they show that context effects can be interpreted as means to achieve decorrelation, and hence, efficient coding.…”