Date of Award
2017
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Barenholtz, Elan
Abstract
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.
Extent
89 p.
Recommended Citation
LaCombe, Daniel C Jr, "Self-Organization of Object-Level Visual Representations via Enforcement of Structured Sparsity in Deep Neural Networks" (2017). Electronic Theses and Dissertations 2010 - 2019. 1782.
https://digitalcommons.fau.edu/etd_general_6/1782
Identifier
FA00004955
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2017.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.