Conference proceeding
A novel model of primary visual cortex based on biologically plausible sparse coding
Proceedings of SPIE--the international society for optical engineering, v 12675, pp 126750M-126750M-6
04 Oct 2023
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.
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Details
- Title
- A novel model of primary visual cortex based on biologically plausible sparse coding
- Creators
- Jocelyn Rego - Drexel UniversityYijing Watkins - Pacific Northwest National LaboratoryGarrett Kenyon - Los Alamos National LaboratoryEdward Kim - Drexel University, Computer Science (Computing)Michael Teti - Los Alamos National Laboratory
- Publication Details
- Proceedings of SPIE--the international society for optical engineering, v 12675, pp 126750M-126750M-6
- Publisher
- SPIE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Scopus ID
- 2-s2.0-85197303398
- Other Identifier
- 991021888815504721