Abstract/Summary

In an extension to research into modeling a biological network of neurons this expands the basic characteristics of an Artificial Neural Network (ANN)computational model to measure functional compensation exhibited by a biological neural network during damage or loss of structure. Whilst current research has highlighted the availability of various technologies and methods relevant to this area of study, none provide a sufficient description as to how fault tolerance is measured nor how damage is evaluated. Such metrics must be consistent, reproducible, and applicable to a plethora of neural network architectures and techniques. Furthermore, measuring fault resilience of biologically inspired ANN architectures provides insight into how biological networks are able to exhibit this amazing ability. This research brings together previous works into a comprehensive damage resilient ANN framework as well as, and more importantly, provides consistent measurement of fault tolerance within this framework. The proposed set of fault resilience metrics provides the means to evaluate the efficacy of networks which are subjectable to damage. These metrics and their source algorithms rely on the modification of various statistical methods and observations currently used for network training optimization.