  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 \emph{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-$\ell_1$ (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.
