Self-Organization of Object-Level Visual Representations via Enforcement of Structured Sparsity in Deep Neural Networks

A hypothesis for the self-organization of receptive fields throughout the hierarchy
of biological vision is empirically tested using simulations of deep artificial neural
networks. Results from many fields for topographic organization of receptive fields
throughout the visual hierarchy remain disconnected. Although extensive simulation
research has been done to model topographic organization in early visual areas, little
to no research has investigated such organization in higher visual areas. We propose
that parsimonious structured sparsity principles, that permit the learning of topographic
receptive fields in simulated visual areas, are sufficient for the emergence of
a semantic topology in object-level representations of a deep neural network. These
findings suggest wide-reaching implications for the functional organization of the biological
visual system and we conjecture that such observed results in nature could
serve as the foundation for unsupervised learning of taxonomic and semantic relations
between entities in the world.