Neural Rhythms
Neural Rhythms
Background:
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.
Objective:
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.
Presentations:
2013 American Controls Conference Presentation
2012 American Controls Conference Simulation
Animations
2013 American Controls Conference Simulation
2012 American Controls Conference Simulation
Papers:
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.
Acknowledgements:
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.