Computer and Mathematical Modelling of Massively Parallel Architectures for Self-Organizing Neural Pattern Recognition Machines

摘要

Substantial progress has been made in several research area. For example, a newclass of neural networks has been developed which are defined by high-dimensional nonlinear dynamics systems that operate at multiple time scales. They are designed to carry out fast, stable autonomous learning of recognition codes and multidimensional maps in response to arbitrary sequences of input patterns. The new neural networks architecture, called ARTMAP, autonomously learns to classify many, arbitrarily ordered vectors into recognition categories based on predictive success. In other research, these investigators developed a new model of temporal prediction that is based upon analysis of how animals and humans learn to time their actions to achieve desired goals. Research was also conducted on the neural dynamics of speech filtering and segmentations, measurement theory, and temporal predictions reinforcement learning, and autonomous credit assignment.