Simulation of stochastic micropopulation models—III. COGNET: An artificial neural network for visual recognition

Abstract

COGNET, based on a neural network first described by Fukushima, demonstrates the relationship between connectionist and other micropopulation models. Its success and physiological orientation led to an implementation using the SUMMERS simulation shell. After self-supervised learning, COGNET uses forward and backward propagation of signals to recognize partial and noisy patterns, and to reconstruct the originals. Stochastic features include variable thresholds for neuronal firing and occasional cell death. The successful implementation of COGNET demonstrates the generality of the concepts embodied in SUMMERS, which in turn promotes the reusability of software and facilitates the extension of computational models in biomedical research. COGNET itself forms a framework for building other physiologically oriented neural network models.