Investigating modularity and transparency within bioinspired connectionist architectures using genetic and epigenetic models

Lacey, George Alexander

Abstract

Machine learning algorithms allow computers to deal with incomplete data in tasks such as speech recognition and object detection. Some machine learning algorithms take inspiration from biological systems due to useful properties such as robustness, allowing algorithms to be flexible and domain agnostic. This comes at a cost, resulting in difficulty when one attempts to understand the reasoning behind decisions. This is problematic when such models are applied in realworld situations where accountability, legality, and maintenance are of concern. Artificial gene regulatory networks (AGRNs) are a type of connectionist architecture inspired by gene regulatory mechanisms. AGRNs are of interest within this thesis due to their ability to solve tasks in chaotic dynamical systems despite their relatively small size.
The overarching aim of this work was to investigate the properties of connectionist architectures to improve the transparency of their execution. Initially, the evolutionary process and internal structure of AGRNs were investigated. Following this, the creation of an external control layer used to improve the transparency of execution of an external connectionist architecture was attempted.
When investigating the evolutionary process of AGRNs, pathways were found that when followed, produced more performant networks in a shorter time frame. Evidence that AGRNs are capable of performing well despite internal interference was found when investigating their modularity, where
it was also discovered that they do not develop strict modularity consistently. A control layer inspired by epigenetics that selectively deactivates nodes in trained artificial neural networks (ANNs) was developed; the analysis of its behaviour provided an insight into the internal workings of the ANN.

Citation

Lacey, G. A. (2022). Investigating modularity and transparency within bioinspired connectionist architectures using genetic and epigenetic models. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4224286

• Thesis Type: Thesis
• Deposit Date: Aug 17, 2022
• Publicly Available Date: Feb 24, 2023
• Keywords: Computer science
• Public URL: https://hull-repository.worktribe.com/output/4224286
• Additional Information: Department of Computer Science and Technology, The University of Hull
• Award Date: Jun 1, 2022