Neural Mechanisms of Nutritional Homeostasis

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Simple and complex animals, including humans, are always faced with the need to decide wisely upon actions using incomplete information. How the nervous system is organized to do this has been a major puzzle. However, we know that animals make most decisions using their own affective state as information, and that in their computations they integrate their own internal state with sensation and experience to arrive at decisions. The result represents a cost-benefit analysis of a behavioral decision in terms of probable benefit, resource loss and possible self-damage. This proposal describes plans to study the neural basis of cost-benefit analysis in decision-making in foraging behavior using a model predator species with simple body form, behavior and nervous system. Three goals are outlined: 1) to find how satiation and prey-avoidance learning influence decision-making mechanisms in the nervous system for attack or avoidance; 2) to determine the role of serotonin, a critical biasing factor, in the mechanisms of decision-making; and 3) to summarize and test the results in a computational model of the neural networks. The expected results are significant to the development of autonomous robots capable of making least-probable-error decisions in a noisy environment, and to the evolution of artificial intelligence for which motivation-based processes may provide the critical regulation of goal related activity, just as in real intelligence systems. The results also relate directly to health issues of weight-control in anorexia and obesity, in that they approach the organization of processes regulating nutritional homeostasis.