9616294 Pender Adaptive neural network control has many applications in the engineering arena, such as stabilization in hypersonic flight Sachs '95 , bioreactor stabilization Ungar '90 , and avionic autolanders Jorgensen '90 . Complex engineering problems in control and system identification require considerable effort form the neural network engineer. Multiple components are involved, including critic, action, and modeling units. These reinforcement networks are capable of solving very difficult dynamic problems. Another fascinating aspect of neuroengineering is applying the concepts of neurocontrol and neuro-identification to model specific areas and activities of the brain. In this work, neural network models based on the adaptive critic will be built up to incorporate the dynamics of avian birdsong learning. The songs themselves are complex sequences of dynamic information, which must be represented internally in the brain as a "modeling" component. The "action" component would be responsible for reproducing or creating a new birdsong, whereas the "critic" component guides the modeling and action components during the learning process. Various parts of the avian brain have been identified as taking part in the learning phase and production phase of avian singing. Multiple factors may be incorporated into the model, as befits the knowledge of neurobiological researchers. Lastly, the neural network models created may help direct neural biologists in their further "wetware" study of the brain. It may provide a model of the general process of learning , which underlies behavior such as the human use of language.
