Abstract and subjects
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 selfsupervised 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.