Abstract
The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.
Highlights
The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex
The findings suggest that a succinct mathematical description how neural populations behave under contextual modulation is possible, and that its characterization can shed light into the image processing computations performed by early visual cortex[54]
We measured the responses of neural populations in mouse primary visual cortex using two-photon imaging (Methods)
Summary
The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Contextual modulation has been studied extensively in single neurons, leading to the development of the influential normalization model[6,46,47] Among several phenomena, this model explains contrast invariance—the finding that the shape of the tuning curve of individual neurons measured at different levels of contrast are scaled versions of each other with responses saturating at high contrasts. This model explains contrast invariance—the finding that the shape of the tuning curve of individual neurons measured at different levels of contrast are scaled versions of each other with responses saturating at high contrasts It offers an account of how tuning curves scale in the presence of a mask that, when presented by itself, does not produce a response. If all neurons in a population are contrast invariant, and if they share the same contrast response function, the direction of the population response will be invariant to the contrast of a visual stimulus[48]
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