PARALLEL IMPLEMENTATION AND CAPABILITIES OF ENTROPY-DRIVEN ARTIFICIAL NEURAL NETWORKS

It is shown how the recently introduced Entropy-Driven Artificial Neural Network Model (EDANN) can be implemented on a parallel transputer array, using a simulation environment which makes all decomposition and mapping issues transparent. Then, using the parallel simulator, the EDANN's capabilities are exemplified in the case of orientation extraction from retinal images. By means of simulations on the parallel machine, it is shown that the EDANN is able to adapt itself optimally to the stimulus it receives, and that the same network topology is able to accomplish both 1-D and 2-D orientation inference tasks. © 1992.