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For the possibility of internships or lab visits, please contact Mario Senden via mario.senden@maastrichtuniversity.nl


Curriculum vitae


Department of Cognitive Neuroscience

Maastricht University

Oxfordlaan 55
6229EV Maastricht





Department of Cognitive Neuroscience

Maastricht University

Oxfordlaan 55
6229EV Maastricht



ConvNets Develop Organizational Principles of the Visual Cortex when using Ganglion Cell-Based Sampling


Journal article


Danny da Costa, R. Goebel, M. Senden
2021

Semantic Scholar DOI
Cite

Cite

APA   Click to copy
da Costa, D., Goebel, R., & Senden, M. (2021). ConvNets Develop Organizational Principles of the Visual Cortex when using Ganglion Cell-Based Sampling.


Chicago/Turabian   Click to copy
Costa, Danny da, R. Goebel, and M. Senden. “ConvNets Develop Organizational Principles of the Visual Cortex When Using Ganglion Cell-Based Sampling” (2021).


MLA   Click to copy
da Costa, Danny, et al. ConvNets Develop Organizational Principles of the Visual Cortex When Using Ganglion Cell-Based Sampling. 2021.


BibTeX   Click to copy

@article{danny2021a,
  title = {ConvNets Develop Organizational Principles of the Visual Cortex when using Ganglion Cell-Based Sampling},
  year = {2021},
  author = {da Costa, Danny and Goebel, R. and Senden, M.}
}

Abstract

The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose that this sampling gives rise to several organizational properties of the primate visual system, including cortical magnification, linear relationship between eccentricity and receptive field sizes, eccentricity-dependent drop-off in spatial-frequency preference, and radial bias. We test this hypothesis by training a convolutional neural network to classify the orientation of sine gratings and Gabor stimuli, resampled according to retinal ganglion cell distributions. Our simulations show that introducing this sampling step gives rise to the aforementioned organizational principles in convolutional layers while only minimally affecting their classification performance. This lends credence to the notion that the retinal ganglion cell distribution is an important factor for the emergence of these organizational principles in visual systems.


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