Per Danzl is the recepient of the 2011 Best PhD Thesis Award in Mechanical Engineering at UCSB, for his dissertation entitled "Dynamical Characterization and Feedback Control of Oscillatory Neural Systems," completed in Spring 2010.

Per Danzl's research focused on applying techniques from controls engineering, dynamical systems theory, and computation to characterize and control spike timing of oscillatory neurons.  This work could ultimately lead to improved treatment of neurological disorders involving pathologically synchronized neurons, such as Parkinson's disease, using the FDA-approved therapeutic technique known as deep brain stimulation.  A particular highlight of Per's research was the development of an event-based feedback control method for randomizing the asymptotic phase of a population of oscillatory neurons using magnitude-constrained stimulus.  Here, a time-optimal stimulus is pre-computed using a dynamic programming approach in the Hamilton-Jacobi-Bellman framework.  Injection of the stimulus waveform is triggered by a population-level spiking event, which drives the neurons to a set of states in which their phases are highly sensitive to naturally occurring noise.  The background noise acts as a phase randomizer, and, over repeated iterations, destabilizes pathological spike synchronization.  Because of its formulation as a feedback control problem, this approach has the benefit of using less power and reducing the possibility of tissue damage and side effects as compared with currently used deep brain stimulation protocols.