The goal of our research is to develop neuro-morphic architectures for implementing Bayes rule. One such architecture is the coupled oscillator feedback particle filter model.
The objective here is to explore phase transition and self organization in large population dynamic systems. A central goal of this research is the largely classical question of emergent behavior in such dynamical systems.
Feedback particle filter is a novel algorithm for nonlinear estimation that has been developed by our research group. It provides for a generalization of the Kalman filter to a general class of nonlinear non-Gaussian problems.