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The geometry of masking in neural populations

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  • Dario L. Ringach

    (Departments of Neurobiology and Psychology, UCLA)

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.

Suggested Citation

  • Dario L. Ringach, 2019. "The geometry of masking in neural populations," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12881-4
    DOI: 10.1038/s41467-019-12881-4
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    Cited by:

    1. Elaine Tring & Mario Dipoppa & Dario L. Ringach, 2023. "A power law describes the magnitude of adaptation in neural populations of primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Rong J. B. Zhu & Xue-Xin Wei, 2023. "Unsupervised approach to decomposing neural tuning variability," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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