Self-processing networks and their biomedical implications

Self-processing networks (connectionist models, neural networks, marker-passing systems, etc.) represent information as a network of interconnected nodes and process that information through the controlled spread of activation throughout the network. Properties characterized as manifestations of intelligence, such as associative recall of stored memories, pattern classification, and learning, are emergent properties; they are global system properties that arise from the concurrent local interactions between the numerous network components. The authors characterize the nature of self-processing networks developed as models of intelligent systems in neuroscience, cognitive science, and artificial intelligence, and contrast them with more traditional information processing models.< >