Topics covered include: membrane transport and diffusion, classical biophysics of the squid giant axon, Markov chain models of single channel gating, cell signal transduction, the buffered diffusion of intracellular calcium, intracellular calcium responses, and excitability, bistability, oscillations, and bursting in a physiological context. We will also consider activity patterns in networks of synaptically coupled neurons, along with specific applications including models for sleep rhythms, Parkinsonian tremor and sensory processing.
Each topic will be studied from the perspective of nonlinear dynamics (either deterministic or stochastic). Mathematical idealizations of each phenomena will be constructed and then analyzed using computer simulation (numerical integration) and graphical techniques (phase- plane analysis).
Computational Cell Biology: An Introduction to Computer Modeling in Molecular Cell Biology. Chris Fall et al., eds. 2002.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Peter Dayan and LF Abbott. 2001.
Mathematical Physiology. James Keener and James Sneyd. 1998.
|Monday, October 1|
|Tuesday, October 2|
|Wednesday, October 3|
|Thursday, October 4|
The first session will begin with a brief history of cell, organ, and culture from the early 1900s to the present and its relationship to modern efforts in cell and tissue engineering. The focus of this hour will be a survey of current and proposed applications of cell and tissue engineering. Using these applications as a starting point, the second hour will be a survey of the recurring approaches to (paradigms) and methods evident in CTE applications. The third hour will cover major challenges in CTE and some promising approaches to dealing with them.
|Thursday, October 18|
|Friday, October 19|
|Thursday, January 10|
|9:00-10:00am||Kurt Thoroughman: Foundations of Neural Computation and Human Motor Behavior|
|10:30-11:30am||Kurt Thoroughman: Foundations of Neural Computation and Human Motor Behavior|
|Friday, January 11|
|9:00-10:00am||Art Kuo: Modeling of Locomotion and Modeling of Biological State Estimation|
|10:30-11:30am||Art Kuo: Modeling of Locomotion and Modeling of Biological State Estimation|
Kurt A. Thoroughman, Washington University in St. Louis
Foundations of Neural Computation and Human Motor Behavior
An initial consideration of quantification of the neural basis of human motor control can be quite attractive: people are easier to talk to than animals, people can perform motor tasks per the instructions of the scientist, and scientists can analyze the performance of people. The next steps, however, contain several conundrums, enigmas, paradoxes, and dilemmas. People dislike having electrodes driven into their brains; functional imaging techniques offer limited spatial and/or temporal resolution. Emergent observable human motor behavior integrates a motley stew of predictive and reactive cortical control, subcortical and spinal circuits, and musculoskeletal biomechanics. In this tutorial I will describe the origins of my prescription for addressing these issues via a computationally-intensive theoretically-and-neurophysiologically-inspired psychophysical approach. Wide-ranging retrospective, circumspective, and prospective questions and discussions are wholeheartedly encouraged.
These tutorials will link with the neuroengineering workshops.
|Thursday, March 27|
|3:30pm||Computer lab demos and discussions|
|Friday, March 28|
|3:30pm||Computer lab demos and discussions|
|Thursday, May 8
Motor circuitry and cellular level activity
|9:00-10:00am||Rachael Seidler: Fundamentals of motor control theory & underlying neuroanatomy|
|10:30-11:30am||Rachael Seidler: Fundamentals of motor control theory & underlying neuroanatomy|
|2:00-3:00pm||Andy Schwartz: Movement parameters reflected in recorded cortical unit activity|
|3:30-4:30pm||Andy Schwartz: Movement parameters reflected in recorded cortical unit activity|
|Friday, May 9
Field potential fundamentals
|9:00-10:20am||Paul Nunez: Fundamentals of the relationship between brain activity and EEGs: Large Scale Brain Physics and Neocortical Dynamic Correlates of Conscious Experience|
|10:30-Noon||Julius P.A. Dewald: Sensorimotor activity reflected in the EEG of able-bodied and paralyzed individuals|
|2:00-3:00pm||William Stacey: Physiology of Epilepsy: Bringing Clinical EEG into the 21st Century|
Paul Nunez, Ph.D., Emeritus Professor of Biomedical Engineering, Tulane University, New Orleans, LA
Fundamentals of the Relationships Between Brain Activity and EEG: Large Scale Brain Physics and Neocortical Dynamic Correlates of Conscious Experience
Spatial-temporal patterns of scalp recorded potentials (electroencephalography or EEG) are determined by the dynamic behavior of current sources in cerebral cortex and volume conduction through head tissue. Volume conduction is governed by a macroscopic version of Poisson's equation, whereas cortical source dynamics originates with delay mechanisms characterized as "local" (e.g., postsynaptic potential rise times) or "global" (finite speed of action potential propagation in cortico-cortical fibers).
All measures of brain function (fMRI, PET, etc.) are highly selective, for example, electrophysiological data recorded from inside the skull are scale-dependent, sensitive to electrode size and location. Scalp potentials are dominated by "synchronized" (phase locked) cortical sources facilitated by cortical anatomy and physiology. Cortical sources of scalp potentials are most conveniently expressed at the mesoscopic spatial scale as current dipole moment per unit volume. The integrated product of this "meso-source" with the head Green's function determines scalp potential.
Human behavior and cognition are believed to originate with cell assemblies (neural networks) embedded in the synaptic source fields that generate EEG. Based on their apparent importance to EEG dynamics, healthy brains may require the following: non-local interactions via cortico-cortical fibers, nested hierarchical structure of cerebral cortex, resonant interactions between cell assemblies at multiple scales, and a proper "balance" between functional segregation and integration controlled by (chemical) neuromodulators.
Rachael Seidler, University of Michigan, Department of Psychology, Division of Kinesiology, Neuroscience Program, & Institute of Gerontology
Fundamentals of Motor Control Theory and Underlying Neuroanatomy
In this tutorial session, I will cover basic motor control theory and neuroanatomy of the motor system. We will discuss methods for measurement of human movement and brain activity, with particular emphasis on techniques that are relevant for brain machine interfaces. Attendees should gain a working understanding of forward and inverse motor control models, efferent copy, state estimation, and their underlying neural correlates. If time permits, we will then delve further into motor system neuroanatomy, including the motor cortical areas (parietal cortex, premotor, supplementary, and cingulate motor areas) as well as basal ganglia thalamocortical loops.
William Stacey, Departments of Epilepsy and Bioengineering, University of Pennsylvania
Bringing Clinical EEG into the 21st Century
Clinical epileptology relies heavily on EEG for diagnosis and treatment. Current practice with EEG is based on 80 years of experience, and has derived from visual classification of the voltage patterns produced by patients with and without epilepsy. One interesting result of this method is that much of clinical EEG is based on recognition of patterns that are poorly understood physiologically. There are many EEG waveforms that have only recently been reconciled with physiology, and many more that are still unexplained. Paradoxically, epileptic seizures are one condition for which the physiology is still poorly understood. Seizure classification, therefore, is a subjective measure that relies on visual inspection and comparison with known patterns and with the patient's "typical background." A seizure is a waveform that a) deviates from the norm b) evolves in frequency and location and c) has clinical or electrical characteristics of a seizure. The subjective nature of this process, as well as the heterogeneity of seizures, makes automated seizure detection a difficult endeavor. An even more difficult problem is seizure prediction, in which early seizure biomarkers might be identified long before the actual seizure begins. Modern EEG equipment now is capable of performing complex analyses and sampling at much higher rates, opening new avenues for analysis that had never been accessible to clinicians. While clinical practice has only begun to utilize this new technology, there are tools from mathematics, engineering, and machine learning that provide intriguing new methods to tap in to this new information.