Successful animals tend to make efficient decisions in their daily activities in terms of effort spent, risk taken and benefit gained. How they do this at the level of the dynamical neuronal circuit has broad implications for the understanding of both normal and abnormal animal and human behavior. This project outlines a plan to describe how the nervous system integrates sensation, internal state and learning.
In preliminary work on a simple animal model system, a simple and robust neural network model has been derived for cost-benefit decision-making. Now, investigations are aimed at explaining the neural bases for switching between orienting and avoidance motor acts, how this switch is determined by sensation, appetite and learning, and how avoidance decision may suppress feeding behavior.
Studies are to be carried out with conventional electrophysiological recording methods. This project contributes to laboratory training of undergraduate and graduate students, and through its content to field classroom education in neuroethology. In these venues an effective synthetic approach, linked by content to problems in animal decision, to neuronal systems and behavior in field and lecture is important in training the next generation of systems and computational neurobiologists. The expected results will elucidate the interactions of goal-directed neuronal network circuitries to show how animals can make successful behavioral decisions that balance perception of available resources and risk against their own needs. They will provide a simple model for approaching the neural circuitry for similar value-based decision-making in more complex animals. The further potential impact of the work is to enable improved ecological and economic modeling using biologically based decision making mechanisms.
