Three Pavlovian conditioning experiments with rats were performed to test predictions from models of associative learning. The procedure in all 3 experiments involved assessing conditioned responding to a Light that was compounded with either perfect or imperfect auditory signals for food. In Experiment 1 a partially reinforced Light which signaled 100% of the food pellets acquired a greater ability to elicit a conditioned response when the auditory cues with which it was paired were relatively poor predictors of the food. In Experiment 2 the Light was still a moderate positive predictor for food, but food was presented both in the presence and absence of the Light. Responding to the Light again was reliably higher when the auditory cues were poor predictors of food. Experiment 3 replicated the results from Experiment 2 and included two control conditions to assess sensitivity to the absolute validity of the Light. In both control conditions the Light was uncorrelated with the US. In one condition the number of food presentations was the same as in the experimental condition, while in the second the number of Light-food pairings was maintained. Results suggested that regardless of the absolute contingency of the Light responding was higher when the auditory cues were uncorrelated with food. These findings suggest that associative models of cue competition make accurate predictions of conditioned behavior following exposure to multiple predictors of reinforcement.

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Research over the past several decades has revealed that the amygdala is involved in aversive, or fear, conditioning. However, the precise nature of this involvement remains a matter of debate. One hypothesis suggests that disrupting amygdala function eliminates the storage of memories formed during aversive conditioning, eliminating the production of internal responses that alter the expression of observable behaviors. Alternatively, lesions or inactivation of the amygdala may impair the modulation of memories in other brain regions and disrupt the ability to perform certain observable behaviors. The experiments reported in the present thesis examined these arguments by making multiple behavioral measures during exposure to unconditioned (US) or conditioned (CS) aversive cues. Amygdala activity was inferred from changes in c-Fos protein expression or activity was temporarily suppressed with muscimol injections. The relationship between the behavioral measures and the role of the amygdala in producing them was examined. Amygdala neurons expressing the c-Fos protein tracked exposure to the US and CS but did not coincide with expression of freezing. Temporary inactivation of the amygdala with muscimol injections before presentation of the US or exposure to the CS attenuated the expression of freezing and active place avoidance; two incompatible behaviors. Finally, temporary inactivation of amygdala activity blocked freezing, place avoidance, and memory modulation produced by the same posttraining exposure to an aversive CS. Since amygdala activation alone was not sufficient to produce freezing and inactivation of the amygdala eliminated freezing, place avoidance, and memory modulation, the results cannot be interpreted as reflecting a direct role for the amygdala in production of observable behaviors. The results also preclude the idea that memory modulation is the only function of the amygdala. Rather, the results of all three studies suggest that the amygdala stores an aversive representation of the US which promotes the expression of various behaviors, possibly through the production of internal responses reflecting an aversive affective state.

While modern engineering systems have become increasingly integrated and complex over the years, interest in the application of control techniques which specifically attempt to formulate and solve the control problem in its inherently uncertain environment has been moderate, at best. More specifically, although many control schemes targeted at Heating, Ventilating and Air-Conditioning (HVAC) systems have been reported in the literature, most seem to rely on conventional techniques which assume that a detailed, precise model of the HVAC plant exists, and that the control objectives of the controller are clearly defined. Experience with HVAC systems shows that these assumptions are not always justifiable, and that, in practice, these systems are usually characterized by a lack of detailed design data and a lack of a robust understanding of the processes involved. Motivated by the need to more efficiently control complex, uncertain systems, this thesis focuses on the development and evaluation of a new neuro-fuzzy model-based predictive control scheme, where certain variables used in the optimization remain in the fuzzy domain. The method requires no training data from the actual plant under consideration, since detailed knowledge of the plant is unavailable. Results of the application of the control scheme to the control of thermal comfort in a simulated zone and to the control of the supply air temperature of an air-handling unit in the laboratory are presented. It is concluded that precious resources (as measured by actuator activity, for example) need not be wasted when controlling these systems. In addition, it is also shown that a very precise (and sometimes not necessarily accurate) control value computed at each sample is unnecessary. Rather, by defining the system and its environment in the fuzzy domain, the fuzzy decision algorithms developed here may be employed to get an "acceptable" control performance.

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