Neural Rhythms

Oscillators, Synchronization, and Signal Processing


Inference (prediction) is believed to be a fundamentally important computational function for biological sensory systems. For example, the Bayesian model of sensory (e.g., visual) signal processing postulates that the cortical networks in the brain encode a probabilistic belief about reality. The belief state (modeled as a posterior distribution in the Bayes; formalism) is updated based on a comparison between the novel stimuli (from senses) and the internal prediction.


A natural question to ask then is whether there is a rigorous methodology (and algorithms) to implement complex forms of prediction (via Bayes theorem) at the level of neurons - the computing elements of the brain. 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. A single oscillator is a simplified model of a single spiking neuron, and the coupled oscillator model represents a neuronal network.


2013 American Controls Conference Presentation

2012 American Controls Conference Simulation


2013 American Controls Conference Simulation

2012 American Controls Conference Simulation


Tilton, A., P. G. Mehta and S. P. Meyn, ''Multi-dimensional Feedback Particle Filter for Coupled Oscillators,'' In the Proceedings of the American Control Conference, Washington DC, 2421-2427, June 2013.

Tilton, A., E. Hsiao-Wecksler and P. G. Mehta, ''Filtering with Rhythms: Application to Estimation of Gait Cycle,'' In the Proceedings of the American Control Conference, Montreal, 3433-3438, June 2012.


Financial support from the Air Force Office of Scientific Research (AFOSR) grant FA9550-09-1-0190 and the National Science Foundation (NSF) grant 0931416 is gratefully acknowledged.