mlsanityData to 7 Oct 2026

Normative theory of visual receptive fields

arXiv 1701.06333 ↗First posted 23 Jan 2017q-bio.NC cs.CVcited by 15

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Abstract (arXiv)

This article gives an overview of a normative computational theory of visual receptive fields, by which idealized functional models of early spatial, spatio-chromatic and spatio-temporal receptive fields can be derived in an axiomatic way based on structural properties of the environment in combination with assumptions about the internal structure of a vision system to guarantee consistent handling of image representations over multiple spatial and temporal scales. Interestingly, this theory leads to predictions about visual receptive field shapes with qualitatively very good similarity to biological receptive fields measured in the retina, the LGN and the primary visual cortex (V1) of mammals.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2017.

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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 5; 2025: 4; 2026: 1.

15 papers.

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  1. 2021Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scalescited by 17
  2. 2023Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fieldscited by 8
  3. 2023Discrete approximations of Gaussian smoothing and Gaussian derivativescited by 7
  4. 2024Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernelscited by 6
  5. 2023Unified theory for joint covariance properties under geometric image transformations for spatio-temporal receptive fields according to the generalized Gaussian derivative model for visual receptive fieldscited by 5
  6. 2024Approximation properties relative to continuous scale space for hybrid discretizations of Gaussian derivative operatorscited by 4
  7. 2024Covariant spatio-temporal receptive fields for spiking neural networkscited by 3
  8. 2024Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variationscited by 3
  9. 2024Relationships between the degrees of freedom in the affine Gaussian derivative model for visual receptive fields and 2-D affine image transformations, with application to covariance properties of simple cells in the primary visual cortexcited by 1
  10. 2025Enhancing CNNs robustness to occlusions with bioinspired filters for border completioncited by 1
  11. 2025The Quest for Universal Master Key Filters in DS-CNNscited by 1
  12. 2021Reconstructing group wavelet transform from feature maps with a reproducing kernel iterationcited by 0
3 more
  1. 2025Modelling and analysis of the 8 filters from the "master key filters hypothesis" for depthwise-separable deep networks in relation to idealized receptive fields based on scale-space theorycited by 0
  2. 2025Hybrid Lie semi-group and cascade structures for the generalized Gaussian derivative model for visual receptive fieldscited by 0
  3. 2026Scale-invariant Gaussian derivative residual networkscited by 0