A novel model of primary visual cortex based on biologically plausible sparse coding

Jocelyn Rego; Yijing Watkins; Garrett Kenyon; Edward Kim; Michael Teti

doi:10.1117/12.2677945

4 October 2023 A novel model of primary visual cortex based on biologically plausible sparse coding

Jocelyn Rego, Yijing Watkins, Garrett Kenyon, Edward Kim, Michael Teti

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750M (2023) https://doi.org/10.1117/12.2677945
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Sparse coding has long been thought of as a model of the biological visual system, yet previous approaches have not employed it as a method to model the activity of individual neurons in response to arbitrary images. Here, we present a novel model of primary cortical neurons based on a biologically-plausible sparse coding model termed the locally-competitive algorithm (LCA). Our hybrid LCA-CNN model, or LCANet, is trained on a self-supervised objective using a standard image dataset and regression models are trained to predict neural activity based on a modern neurophysiological dataset containing the responses of hundreds of neurons to natural image stimuli. Our novel sparse coding model better represents the computations performed by biological neurons and is significantly more interpretable than previous models.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jocelyn Rego, Yijing Watkins, Garrett Kenyon, Edward Kim, and Michael Teti "A novel model of primary visual cortex based on biologically plausible sparse coding", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750M (4 October 2023); https://doi.org/10.1117/12.2677945

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