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Orientation selectivity properties for integrated affine quasi quadrature models of complex cells
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). (Computational Brain Science Lab)ORCID iD: 0000-0002-9081-2170
2025 (English)Report (Other academic)
Abstract [en]

This paper presents an analysis of the orientation selectivity properties of idealized models of complex cells in terms of affine quasi quadrature measures, which combine the responses of idealized models of simple cells in terms of affine Gaussian derivatives by (i) pointwise squaring, (ii) summation of responses for different orders of spatial derivation and (iii) spatial integration. Specifically, this paper explores the consequences of assuming that the family of spatial receptive fields should be covariant under spatial affine transformations, thereby implying that the receptive fields ought to span a variability over the degree of elongation.

We investigate the theoretical properties of three main ways of defining idealized models of complex cells and compare the predictions from these models to neurophysiologically obtained receptive field histograms over the resultant of biological orientation selectivity curves. It is shown that the extended modelling mechanisms lead to more uniform behaviour and a wider span over the values of the resultat that are covered, compared to earlier presented idealized models of complex cells without spatial integration.

More generally, we propose (i) to include a variability over the degree of elongation of the receptive fields in functional models of complex cells, and that (ii) the presented methodology with comparisons to biological orientation selectivity curves and orientation selectivity histograms could be used as a new tool to evaluate other computational models of complex cells in relation to biological measurements.

Place, publisher, year, edition, pages
2025. , p. 12
Keywords [en]
receptive field, complex cell, orientation selectivity, elongation, affine covariance, vision
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-361804DOI: 10.48550/arXiv.2503.21611OAI: oai:DiVA.org:kth-361804DiVA, id: diva2:1948159
Projects
Covariant and invariant deep networks
Funder
Swedish Research Council, 2022-02969Available from: 2025-03-28 Created: 2025-03-28 Last updated: 2025-04-04

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Citation style
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