BELBIC

Abstract: We describe work in progress with the aim of constructing a computational model of emotional learning and processing inspired by neurophysiological findings. The main brain areas modeled are the amygdala and the orbitofrontal cortex and the interaction between them. We want to show that (1) there exists enough physiological data to suggest the overall architecture of a computational model, (2) emotion plays a clear role in learning the behavior. We review neurophysiological data and present a computational model that is subsequently tested in simulation.

Abstract: The amygdala has repeatedly been implicated in emotional reactions and in learning of new emotionally significant stimuli. The system forms an important part of motor learning as well as attention. This paper presents a neurologically inspired computational model of the amygdala and the orbitofrontal cortex that aims to partially reproduce the same characteristics as the biological system. This model has been tested in simulations, the results of which are presented.

Abstract: In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for PID+BELBIC.

Abstract: The amygdala is a small subcortical structure that has long been implicated in the conditioning of fear and other emotions. It is heavily interconnected to a number of both cortical and subcortical structures and is thus well placed to integrate sensory inputs from multiple areas to produce emotional reactions directly as well as influence learning and attention systems. Data suggests that the amygdala works in close cooperation with the orbitofrontal cortex; the amygdala learns emotional reactions to stimuli, while the orbitofrontal cortex learns to inhibit the reactions from the amygdala in a context-sensitive manner. The hippocampus is encoding the contextual representations that are used by the orbitofrontal cortex. Being responsible for the conditioning of emotional reactions, the amygdala forms a part of a conceptual system integrating emotions, motivation and actions. The thesis briefly discusses this system, and also reviews the neurophysiological and neuroanatomical features of the amygdala, the orbitofrontal cortex and related areas. As a learning system, data suggests the amygdala is working as a classical conditioning system. This system is used both to elicit autonomous reactions to emotional stimuli directly through the central amygdala, and as an evaluative part of an instrumental conditioning system through the basolateral amygdala. Through this mechanism, the structure is also involved in selective memory consolidation and in selective priming of stimuli in the sensory cortices. Classical and instrumental conditioning is discussed, and a number of computational models of classical conditioning are presented and compared. The second half of the thesis presents a computational model of the amygdala and orbitofrontal cortex. The model has a very simple design for each component; much of the abilities of the model instead comes from the neuroanatomically guided interconnections between these components. The model is tested with only the amygdala and orbitofrontal cortex, and then extended with a simple hippocampal model able to generate contextual signals from an externally imposed attentional sequence. The model is compared to the previously tested conditioning models and its benefits and drawbacks - especially its current inability to handle time-dependent effects - are discussed.