Roby Velez

University of Wyoming | UW · Department of Computer Science

As the power and capabilities of Artificial Neural Networks (ANNs) grow, so do their size and complexity. To both decipher and improve ANNs, we need to build better tools that help us understand their inner workings. To that end, we introduce an algorithm called Subsets Regression on network Connectivity (SRC). SRC allows us to prune away unimporta...

20 random CFNs for the best and worst PA individuals. Each block contains the unsimplified version of the individual followed by 20 of its CFNs. (TIFF)

Differences between this work and Ellefsen et al. [16]. Differences prevent direct comparison between the non-diffusion treatments in this work and the networks in Ellefsen et al. [16]. Purpose of many of the changes were to make it easier for modular solutions to appear in order to investigate whether they aid with catastrophic forgetting. (TIFF)

20 random CFNs for the best and worst PA_D individuals. Each block contains the unsimplified version of the individual followed by 20 of its CFNs. (TIFF)

20 random CFNs for the best and worst PCC_D individuals. Each block contains the unsimplified version of the individual followed by 20 of its CFNs. (TIFF)

20 random CFNs for the best and worst PCC individuals. Each block contains the unsimplified version of the individual followed by 20 of its CFNs. (TIFF)

Network topology. Individuals in the foraging task are represented as ANNs where each node possesses an (x, y) position. The first three inputs correspond to food items while the last two inputs are fed positive (1) and negative (−1) feedback signals for the summer and winter season respectively. An output greater than 0 results in the agent eating...

Plot of all seasonal associations. See main text for description and interpretation. For further details on seasonal associations see Ellefsen et al. [16]. (TIF)

A 1-connection knockout in the Core Functional Networks (CFNs) confirms that ARK properly identifies functional modules. The original summer and winter fitness for all CFNs is plotted along with the summer and winter fitness after the knockout of a random, common, winter, or summer functional connection. The original (no connection) and random conn...

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artifi...

Novelty Search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. Instead of select-ing for phenotypes that are closer to an objective, Novelty Search assigns rewards based on how different the pheno-types are from those already generated. A common criticism of Novelty Search is that it is effectively random or exh...

An exposure system was previously developed to quantify destruction of bacterial biofilms by 1 MHz c.w. ultrasound at 0.8 MPa peak-to-peak acoustic pressure amplitude (JASA 122(5):3052, 2007). Bacterial killing is quantified via confocal microscopy using florescent E. Coli and image processing. Recently, a passive detector of inertial and stable ca...

Biofilms of florescent E. coli bacteria were grown in protein‐rich media on coverslips that served as the floor of an ultrasound exposure chamber. Two opposite walls of the chamber were strips of lead zirconate titanate (PZT‐4) driven at their resonance frequency of 810 kHz in continuous wave mode, thereby setting up acoustic standing waves within...