2023/02/22 by Jonathan Huml, Huml, Jonathan, Abiy Tasissa +3
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · #Artificial Intelligence (cs.AI) #CCD and CMOS Imaging Sensors #Cell Image Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.2302.11162
openalex publication_date 2023/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data. However, the Gabor-like filters learned by classical sparse coding far overpredict well-tuned simple cell receptive field (SCRF) profiles. A number of subsequent models have either discarded the sparse dictionary learning framework entirely or have yet to take advantage of the surge in unrolled, neural dictionary learning architectures. A key missing theme of these updates is a stronger notion of structured sparsity. We propose an autoencoder architecture whose latent representations are implicitly, locally organized for spectral clustering, which begets artificial neurons better matched to observed primate data. The weighted-ℓ1 (WL) constraint in the autoencoder objective function maintains core ideas of the sparse coding framework, yet also offers a promising path to describe the differentiation of receptive fields in terms of a discriminative hierarchy in future work.