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| Goodman Brain Computation Lab: Related Classes |
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| Course Number | Classes Info |
| BIOL 660 Biology |
IMMUNOBIOLOGY Credits: (3+0) 3 |
| Organization, development, and regulation of the immune system, antibody structure and function, cellular and humoral effector mechanisms in immunity, and immunologic mechanisms in disease. Prerequisite: BIOL 313 or BIOL 315, or equivalent (FALL ONLY) Instructor: Prof. Hunter | |
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| BME 770 BioMedical Engineering |
RESEARCH ROTATION Credits: (0+9) 3 |
| Research practicum in a laboratory that compliments primary research area. | |
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| BME 780 BioMedical Engineering |
RESEARCH PROPOSAL PREPARATION Credits: (0+9) 3 |
| Preparation of a research grant proposal on dissertation topic or any other BME research topic using a format defined by a major by national funding agency. | |
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| BME 790 BioMedical Engineering |
SEMINAR Credits: (3+0) 3 |
| Reports and critical analysis of current research. Maximum of 6 credits. Limited to BME majors. | |
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| BME 791 BioMedical Engineering |
SPECIAL TOPICS Credits: 1 to 3 |
| Special study of advanced topics in Biomedical Engineering. Maximum of 6 credits. Limited to BME majors | |
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| BME 793 BioMedical Engineering |
INDEPENDENT STUDY Credits: 1 to 3 |
| Selected problems in Biomedical Engineering. Maximum of 6 credits. Limited to BME majors. | |
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| BME 794 BioMedical Engineering |
COLLOQUIUM Credits: (3+0) 3 |
| Presentation and analysis of original research. Maximum of 6 credits. Limited to BME majors. | |
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| BME 797 BioMedical Engineering |
THESES Credits: 1 to 6 |
| for BME majors in the master's program only. | |
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| CS 477,677 BioMedical Engineering |
ARTIFICIAL INTELLIGENCE PROGRAMMING Credits: (3+0) 3 |
| credits Programming in an artificial intelligence language. Examples from intelligent agents, connectionist models, and expert systems. Prerequisite: CS 476. | |
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| MATH 753 BioMedical Engineering |
STOCHASTIC MODELS AND SIMULATION Credits: (3+0) 3 |
| credits Stochastic process models with applications. Analytic and computer modeling techniques for Markov chains. Poisson processes, Markov processes, Brownian motion, and special topics. Prerequisite: MATH 311, 330, 352 (FALL ONLY) Instructor: Prof. Meerschaert | |
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| MATH 420,620 BioMedical Engineering |
MATHEMATICAL MODELING Credits: (3+0) 3 |
| Formulation, analysis and critique of methods of mathematical modeling; selected applications in physics, biology, economics, political science and other fields. Prerequisite: MATH 179 or 283 and 352. (Major capstone course.) (SPRING ONLY) Instructor: Prof. Meerschaert | |
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| PCB 603 BioMedical Engineering |
MEDICAL NEUROPHYSIOLOGY Credits: (3+0) 3 |
| Introduction to medical neuroscience covering topics in cellular neurophysiology, sensory function, motor systems function, cognition, memory and behavioral neuroscience (SPRING ONLY) Instructors: VonBartheld , Kenyon , Goodman | |
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| BIOL 475,675 BioMedical Engineering |
NEUROBIOLOGY Credits: (3+3) 4 |
| Basic neurosciences: characteristics of excitable tissues, central nervous mechanisms in sensation, neural control of movement, functional neuroanatomy. Prerequisite: BIOL 316 (FALL ONLY) Instructor: Prof. Mastick | |
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| CS 467,667 Computer Science |
THEORY OF COMPUTATION Credits: (3+0) 3 |
| Fundamental concepts of computation. Relationship between grammars, languages and machines, emphasizing regular and context free languages, finite state acceptors and Turing machines. Complexity and computability. Prerequisite: MATH 182 and CS 306. | |
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| CS 415,615 Computer Science |
PARALLEL COMPUTING Credits: (3+0) 3 |
| Parallel algorithms and architectures. Taxonomy of systems, SIMD, MIMD, systolic arrays. Parallel languages and programming paradigms. Applications using a multiple processor parallel network. Prerequisite: CS 308 | |
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| CS 476,676 Computer Science |
ARTIFICIAL INTELLIGENCE Credits: (3+0) 3 |
| Problem solving, search, and game trees. Knowledge representation, inference, and rule-based systems. Semantic networks, frames, and planning. Introduction to machine learning, neural-nets, and genetic algorithms. Prerequisite: CS 308. | |
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| CS 479,679 Computer Science |
PATTERN RECOGNITION Credits: (3+0) |
| Pattern recognition systems, statistical methods, discrimination functions, clustering analysis, unsupervised learning, feature extraction and feature processing. Prerequisite: MATH 352 | |
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| CS 491,691 Computer Science |
TOPICS Credits: 1 to 3 |
| (a) Advanced graphics, (b) architecture, (c) biomedical computing, (d) compilers, (e) computational science, (f) computer-aided design, (g) computer networks, (h) database systems, (j) fuzzy logic, (k) genetic algorithms, (m) human-computer interaction, (n) knowledge-based systems, (p) logic foundations, (q) machine learning, (r) modeling and simulation, (s) neural networks, (t) object-oriented programming, (u) operating systems, (v) parallel computing, (w) programming languages, (x) robotics, (y) scientific visualization, (z) software engineering. May be repeated for different topics. Prerequisite: Junior level course work in CS. | |
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| CS 709a Computer Science |
Cryptography Credits: (3+0) 3 |
| Algorithms and math methods of large numbers.(SPRING ONLY) Instructor: John Black | |
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| CMPP 740 Medical School |
NEUROEFFECTOR PHARMACOLOGY Credits: (3+0) 3 |
| The course will cover topics in cell signaling with particular attention to mechanisms important in neurobiology. Specific topics will include neurotransmitters and their effects, mechanisms of transmitter release and storage, IP3 and GTPases in signaling, regulation of voltage-gated potassium channels by kinases and phosphatases, neurotrophic factors and their receptors, excititoxicity, and long-term potentiation and depression. Much of the course has been inspired by articles in a recent issue of Current Opinion in Neurobiology (Volume: 11, Issue: 3, June 1, 2001. Instructor: Dr. James Kenyon | |
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