Dynamics of Motor Behavior Laboratory
DMBL studies neural mechanisms involved in controlling rhythmic motor behaviors like walking, swimming, or chewing. Such behaviors are generated by neural networks called central pattern generators (CPGs) that can still produce motor patterns even when sensory feedback is absent.
The goals of DMBL are:
1. Understand how small biological CPGs produce rhythmic motor behavior that is also highly variable and adaptive.
2. Analyze and understand the contributions of noise and unstable (hence often chaotic) dynamics to the variability observed in all parts of the nervous system but particularly in small CPGs.
3. Build small hardware robotic mechanisms to illustrate and augment our understanding of CPG dynamics.
Current projects of DMBL are:
1. Building computer models of neurons and networks to study the influence of biological noise and unstable dynamics on motor pattern generation.
2. Using established and novel methods for the analysis of variable neural data.
3. Building a controller for a simple mobile robotic mechanism that is inherently unstable.
A brief Curriculum Vitae.
Rowat, Peter, and Greenwood, Priscilla (2011) " Identification and continuity of the distributions of burst-length and inter-spike-intervals in the stochastic Morris-lecar neuron. " Neural Computation 23, 3094-3124, 2011
Lainscsek C, Rowat P, Schettino L, Lee D, Song D, Letellier C, and Poizner H " Nonlinear DDE analysis of repetitive hand movements in Parkinson's disease. " Chaos 22(1), 013119 March 2012 Reprinted in Virtual Journal of Biological Physics Research / Volume 23 / Issue 5 / STATISTICAL AND NONLINEAR PHYSICS
Rowat (2007) "Inter-spike interval statistics in the stochastic Hodgkin-Huxley model: coexistence of gamma frequency bursts and highly irregular firing. " Neural Computation 19, 1215-1250, 2007
Rowat and Elson (2004) "State-Dependent Effects of Na Channel Noise on Neuronal Burst Generation. " J. Computational Neuroscience 16, 87-112, 2004
Rowat and Selverston (1997b) "Synchronous Bursting Can Arise from Mutual Excitation, even when individual cells are not endogenous bursters. " J. Computational Neuroscience 4, 129-139, 1997
Rowat and Selverston (1997a)"Oscillatory mechanisms in pairs of neurons connected with fast inhibitory synapses. " J. Computational Neuroscience 4, 103-127, 1997
Doya, Selverston, and Rowat (1994) " A Hodgkin-Huxley Type Neuron Model that Learns Slow Non-Spike Oscillation " J.D.Cowan, G.Tesauro, and J. Alspector (eds.), Advances in Neural Information Processing Systems 6, pp.566-573, Morgan Kaufmann, CA, 1994
Rowat and Selverston (1993) " Modeling the gastric mill central pattern generator of the lobster with a relaxation-oscillator network. " J. Neurophysiology 70(3), 1030-1053, 1993
Rowat and Selverston (1991) " Learning algorithms for oscillatory networks with gap junctions and membrane currents. " Network 2, 17-41, 1991
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