Synthetic neural modeling: the `Darwin´ series of recognition automata

The authors describe how the theory of neuronal group selection (TNGS) can form the basis for an approach to computer modeling of the nervous system. Three examples of synthetic neural modeling are discussed. Darwin I was designed to examine the process of pattern recognition and some general factors relating to degeneracy and amplification in selective systems. Darwin II introduced recognition units with some of the properties of neuronal groups, connected in reentrant networks to permit exploration of aspects of the recognition process leading to categorization, generalization, and associative memory. Darwin III is a behaving automation whose behavior is not programmed but results from its encounter with events in its world under constraints of neuronal and synaptic selection. The oculomotor, reaching, touch, and categorization subsystems of the Darwin-III system are discussed. Darwin III is compared to models based on neurobiology, models based on artificial intelligence, and connectionist models