Author Type

Graduate Student

Date of Award

2022

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Barenholtz, Elan

Thesis/Dissertation Co-Chair

Hahn, William

Abstract

Although state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust, sparse representations of sensory stimuli for downstream tasks. In this thesis, I hypothesize that CNN layers equipped with lateral competition better approximate the response characteristics and dynamics of neurons in the mammalian primary visual cortex, leading to increased robustness under noise and/or adversarial attacks relative to current robust CNN layers. To test this hypothesis, I develop a new class of CNNs called LCANets, which simulate recurrent, feature-specific lateral competition between neighboring neurons via a sparse coding model termed the Locally Competitive Algorithm (LCA). I first perform an analysis of the response properties of LCA and show that sparse representations formed by lateral competition more accurately mirror response characteristics of primary visual cortical populations and are more useful for downstream tasks like object recognition than previous sparse CNNs, which approximate competition with winner-take-all mechanisms implemented via thresholding.

Extent

118 p.

Comments

Includes bibliography.|Dissertation (PhD)--Florida Atlantic University, 2022.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014050

Format

pdf

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