|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
This course provides a comprehensive background in biomaterials. It covers surface properties, mechanical behavior and tissue response of ceramics, polymers and metals used in the body. It also builds on this knowledge to address aspects of tissue engineering, particularly the substrate component of engineering tissue and organs.
-
3.00 Credits
Prerequisite(s): Graduate Standing or permission of the instructor. Introduction to cell and molecular biology with emphasis on quantitative concepts and applications to multicellular systems.
-
3.00 Credits
Prerequisite(s): ESE 310 and ESE 325 or equivalent. A modern introduction to the physical principles of optical imaging with biomedical applications. Propagation and interference of electromagnetic waves. Geometrical optics and the eikonal. Plane-wave expansions, diffraction and the Rayleigh criterion. Scattering theory and the Born approximation. Introduction to inverse problems. Multiple scattering and radiative transport. Diffusion approximation and physical optics of diffusing waves. Imaging in turbid media. Introduction to coherence theory and coherence imaging. Applications will be chosen from the recent literature in biomedical optics.
-
3.00 Credits
Finkel. Cellular level simulation of neurons at the biophysical level. Topics include cable theory, the Hodgkin-Huxley formalism for different channelspecies , synaptic interactions and plasticity, information measures in network activity, neuromodulation, and applications to modeling neurological disease.
-
3.00 Credits
Finkel. Computational modeling and simulation of the structure and function of brain circuits. A short survey of the major ideas and techniques in the neural network literature. Particular emphasis on models of hippocampus, basal ganglia and visual cortex. A series of lab exercises introduces techniques of neural simulation.
-
3.00 Credits
Prerequisite(s): BE 301 (Signals and Systems) or equivalent, computer programming experience, preferably MATLAB (e.g., as used the BE labs, BE 209/210/310). Some basic neuroscience background (e.g. BIOL 215, BE 305, BE 520, INSC core course), or independent study in neuroscience, is required. This requirement may be waived based upon practical experience on a case by case basis by the instructor.The course is geared to advanced undergraduate and graduate students interested in understanding the basics ofimplantable neuro-devices, their design, practical implementation, approval, and use. Reading will cover the basics of neuro signals, recording, analysis, classification, modulation, and fundamental principels of Brain-Machine Interfaces. The course will be based upon twic weekly lectures and "hands-on" weekly assignments that teach basic signal recording, feature extraction, classification and practical implementation in clinical systems. Assignments will build incrementally toward constructing a complete, functional BMI system. Fundamental concepts in neurosignals, hardware and software will be reinforced by practical examples and in-depth study. Guest lecturers and demonstrations will supplement regular lectures.
-
3.00 Credits
Prerequisite(s): Math through multivariate calculus (MATH 241), programming experience, as well as some familiarity with linear algebra, basic physics, and statistics.
-
3.00 Credits
Physiology and anatomy of living neurons and neural networks; Brain organization; Elements of nonlinear dynamics, the driven pendulum as paradigm for complexity, synchronicity, bifurcation, self-organization and chaos; Iterative maps on the interval, period-doubling route to chaos, universality and the Feigenbaum constant, Lyapunov exponents, entropy and information; Geometric characterization of attractors; Fractals and the Mandelbrot set; Neuron dynamics: from Hudgkin-Huxley to integrate and fire, bifurcation neuron; Artificial neural networks and connectionist models, Hopfield (attractor-type) networks,energy functions, convergence theorems, storage capacity, associative memory, pattern classification, pattern completion and error correction, the Morita network; Stochastic networks, simulated annealing and the Boltzmann machine, solution of optimization problems, hardware implementations of neural networks; the problem of learning, algorithmic approaches: Perception learning, back-propagation, Kohonnen's self-organizing maps and other networks; Coupled-map lattices; Selected applications including financial markets.
-
3.00 Credits
This course provides an introduction to the quantitative methods used in characterizing and engineering biomolecular properties and cellular behavior, focusing primarily on receptor-mediated phenomena. The thermodynamics and kinetics of protein/ligand binding are covered, with an emphasis on experimental techniques for measuring molecular parameters such as equilibrium affinities, kinetic rate constants, and diffusion coefficients. Approaches for probing and altering these molecular properties of proteins are also described, including site-directed mutagenesis, directed evolution, rational design, and covalent modification. Equilibrium, kinetic, and transport models are used to elucidate the relationships between the aforementioned molecular parameters and cellular processes such as ligand/receptor binding and trafficking, cell adhesion and motility, signal transduction, and gene regulation.
-
3.00 Credits
This course covers the fundamentals of modern techniques in biological and in vivo biomedical imaging. This practical course consists of a series of hands-on lab exercises, covering major imaging modalities, but also extends to non-radiology modalities of interest in biological, pathological or animal imaging (e.g., optical imaging). Topics include x-ray, mammography, MRS, MRI, PET, and ultrasound. The emphasis will be on hands-on aspects of all areas of imaging and imaging analysis. Small groups of students will be led by a faculty member with technical assistance as appropriate.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|