2-credit course for intro neurosci students. Topics include: basic spike train analysis, basic computational models of spiking, the Hodgkin-Huxley model of spiking, an introduction to Fourier series, and an introduction to networks. Meets twice a week, with one lecture and one computer lab.
2-credit course for more mathematically advanced neurosci students. Topics include introductions to: correlation and coherence analysis, cross frequency coupling, and bifurcations in model neurons. Meets twice a week, with one lecture and one computer lab.
Introduces the theory of point processes and develops practical problem-solving skills to construct models, assess goodness-of-fit, and perform estimation from point process data.
Focuses on signal processing methods for the analysis of stochastic dynamical systems in neuroscience. Includes state-space methods and dynamic Baysian methods applied to continuous and point process observations.
A survey of statistical methods for neuroscience research. Core topics include introductions to the theory of point processes, the generalized linear model, Monte Carlo methods, Bayesian methods, multivariate methods, time-series analysis, spectral analysis and state-space modeling. This course was developed jointly with Eden and a version is being planned for BU. (Eden)
Introduction to the connections and distinctions among various imaging modalities (ultrasound, MRI, EEG, optical), common goals of biomedical imaging, broadly defined target of biomedical imaging, and the current practical and economic landscape of biomedical imaging research.
Tools and methods for analyzing brain dynamics, which was attended by undergraduate and graduate students throughout the CRC network.
Special seminar on how to get neuroengineering innovations out into the world.
Introduction to modeling neurons and neuronal networks using differential equations. Hodgkin-Huxley equations; phase plane analysis and bifurcation theory applied to neuronal models; reduced models (integrate-and-fire neurons, theta neurons); modeling chemical and electrical synapses; synchronization, rhythms, and waves in neuronal networks.