Martin, Gregory A.

Alopex is a biologically influenced computation paradigm that uses a stochastic procedure to find the global optimum of linear and nonlinear functions. It maps to a hierarchical SIMD (Single-Instruction-Multiple-Data) architecture with simple neuronal processing elements (PE's), therefore the large amount of interconnects in other types of neural networks are not required and more efficient utilization of chip level and board level "real estate" is realized. In this study, verifications were performed on the use of a simplified Alopex algorithm in handwritten digit recognition with the intent that the verified algorithm be digitally implementable. The inputs to the simulated Alopex hardware are a set of 32 features extracted from the input characters. Although the goal of verifying the algorithm was not achieved, a firm direction for future studies has been established and a flexible software model for these future studies is available.