Intentional Navigation and Phase Transition Analysis in Amygdala of KIV Model

Previous study on the KIV model proved that it is feasible to extract information from the amygdala for navigation tasks. In this work, we observed the chaotic dynamics of the KIV model by analyzing the global phase transitions that occur in the amygdala. By using the Hilbert transform, we are able to capture the fast global synchronized spatial patterns of amplitude modulation (AM) in the Amygdala. The ultimate goal for identifying the phase transition within the amygdala is to aid the system for decision-making while navigating in a challenging environment with an intentional behavior. In this paper, we will concentrate on the phase transition analysis in the amygdala of the KIV model. Our experiment has shown the detection of phase transition in the amygdala can be done by Hilbert transform.