# Henry Greenside

## Professor

## Faculty Network Member of Duke Institute for Brain Sciences

## Details

** Characterization of the domain chaos convection state by the largest Lyapunov exponent
**

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
(2006)

** Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model
of songbird nucleus HVC
**

Physical Review E
(2006)

** Characterization of the domain chaos convection state by
the largest Lyapunov exponent
**

Physical Review E
(2006)

** Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC.
**

Phys Rev E Stat Nonlin Soft Matter Phys
(2006)

** Enhanced tracer transport by the spiral defect chaos state of a converting fluid
**

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
(2005)

** Enhanced tracer transport by the spiral defect chaos state of a convecting fluid
**

Physical Review E
(2005)

** Efficient algorithm on a nonstaggered mesh for simulating Rayleigh-BÃ©nard convection in a box
**

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
(2003)

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After working in nonlinear dynamics and nonequilibrium pattern formation for many years, my research group has begun studying problems in theoretical neurobiology in collaboration with Professor Richard Mooney's experimental group on birdsong at Duke University. The main scientific question we are interested in is how songbirds learn to sing their song, which is a leading experimental paradigm for the broader neurobiology question of how animals learn behaviors that involve sequences of time. My group is interested in problems arising at the cellular and network levels (as opposed to behavioral levels). One example is understanding the origin, mechanism, and eventually the purpose of highly sparse high-frequency bursts of spikes that are observed in the nucleus HVC of songbird brains (this is the first place where auditory information seems to be combined with motor information). A second example is to understand how auditory and motor information are combined, e.g., there are data that suggests that the same group of neurons that instruct the respiratory and syringeal muscles to produce song (again in nucleus HVC) are also involved in recognizing song. A third example is trying to understand changes in anatomy (increases in spine stability) that were recently observed in living brain tissue as a bird learns its song.

**Education:**

MS - Princeton

M.A. - Princeton University

B.A. - Harvard University

** Simulating Complex Dynamics In Intermediate And Large-Aspect-Ratio Convection Systems
**

In 18th Symposium on Energy Engineering Sciences edited by . ; : Argonne National Laboratory.

** Spatiotemporal Chaos in Large Systems: The Scaling of Complexity With Size
**

In Semi-Analytic Methods for the Navier Stokes Equations edited by K. Coughlin. ; pp. 9-40. : American Mathematical Society, Providence, RI.

** The Spatiotemporal Dynamics of Generalized Tonic-Clonic Seizure EEG Data: Relevance To the Clinical Practice of Electroconvulsive Therapy
**

In Nonlinear Dynamics in Brain Function edited by . ; : In Press.

** The EEG Effects of ECT: Implications for rTMS
**

In Progress in Neuropsychopharmacology and Biological Psychiatry edited by . ; : In Press.

**2001**Barbara and Randall Smith Duke Faculty Enrichment Award