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Spring 2003 First day of classes: Pitt January 6 2003; CMU January 13 2003. Core courses: Computational Models of Neural Systems,
CMU Biological Sciences 03-315 Magnetic Resonance Imaging in Neuroscience: 9 units
This course introduces the fundamental principles of magnetic resonance imaging (MRI) and its application in neuroscience. MRI is emerging as the preeminent method to obtain structural and functional information about the living human brain. This methodology has helped to revolutionize neuroscience and the study of human cognition. The specific topics covered in this course will include: introduction to spin gymnastics, survey of imaging methods, structural brain mapping, and function MRI (fMRI). Guest lectures will be incorporated into the course from neuroscientists and psychologists who use MRI in their own research. CMU Computer Science 15-485/785 Computational Perception and Scene
Analysis: 12 units
This course teaches advanced aspects of perception, scene analysis, and recognition in both the visual and auditory modalities, concentrating on those aspects that allow us and animals to behave in natural, complex environments. The goal of this course is to teach how to reason scientifically about problems and issues in perception and scene analysis, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. Specific topics include sensory coding, perceptual invariance, spatial vision and sound localization, visual and auditory scene segmentation, many aspects of attention, and the basics of recognition in natural visual and auditory scenes. Mathematical topics covered include Bayesian inference, information theory, linear systems analysis, neural networks, independent component analysis, and various algorithms in computational vision and audition. 15-883 Computational Models of Neural Systems: 12 units [CNBC Core Course]
This course offers an in-depth look at biological neural systems from a computational perspective. We will examine a variety of brain structures whose anatomy and physiology are sufficiently well understood that it's possible to theorize about the representations and algorithms they employ. There will be some neuroscience tutorial lectures for those with no prior background in this area. Students will also have the opportunity to experiment with some actual computational models running in Matlab. CMU Psychology 85-719 Introduction to Parallel Distributed Processing: 9 units [CNBC Core Course]
This course provides an overview of parallel distributed processing (PDP) models of aspects of perception, memory, language, knowledge representation, and learning. The course consists of lectures describing the mathematical and computational theory behind artificial neural network models as well as their implementation. Students also acquire substantial hands-on experience manipulating existing simulation models on computer workstations, and they are expected to complete term projects involving novel simulation work. Prerequisites include course 85-211 (Cognitive Psychology), extensive experience using computers, and course 21-122 (Calculus 2) or permission of the instructor.
85-735/10-731 Computational Analyses of Brain Imaging Psychology: 12 Units
With the growing flood of brain imaging data, computer methods play an increasingly critical role in extracting information from the experimental results and in characterizing the patterns of brain activity. This course will survey computer methods for analyzing brain imaging data, ranging from widely used software for signal processing and visualization (e.g., AFNI, Fiasco, Brain Voyager, SPM), to approaches that have just recently seen growing use (e.g., Independent Components Analysis), to research on new methods (e.g., machine learning approaches to decoding mental states), to research on computational cognitive models (e.g., 4CAPS, ACT-R). We'll look at the problems of computer analysis from the perspective of the scientific questions that underlie cognitive neuroscience. The course work will consist of presenting and learning from tutorial sessions on the methods, and applying the methods in small projects. 85-770 Perception: 9 Units
Perception, broadly defined, is the construction of a representation of the external world, for purposes of thinking about it and acting in it. Although we often think of perception as the processing of inputs to the sense organs, the world conveyed by the senses is ambiguous, and cognitive and sensory systems interact to interpret it. In this course, we will examine the sensory-level mechanisms involved in perception by various sensory modalities, including vision, audition, and touch. We will learn how sensory coding interacts with top-down processing based on context and prior knowledge and how perception changes with learning and development. The goals include not only imparting basic knowledge about perception, but fostering an appreciation for the beauty of perceptual systems and providing some new insights into everyday experiences.
CMU Robotics 16-725 Methods in Medical Image Analysis: 12 units
The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.Summer research funding to implement a new algorithm in ITK is available for 3 students upon completion of the course. You can take a look at the toolkit at http://www.itk.org CMU Statistics 36-746 Statistical Methods for Neuroscience: 12 units
This course provides a brief survey of statistical methods that are of use in cognitive neuroscience. The first part of the course will present a compressed version of material often covered in a semester-long course in elementary statistics. The latter part of the course will introduce various more advanced methods. Topics include Probability (laws of probability, conditional probability, Bayes' Theorem, random variables, Binomial, Poisson, and Normal distributions, and Poisson and other point processes), Exploratory Data Analysis (Descriptive methods for single samples and multiple samples, scatterplot smooths, histograms, and density estimators), Elementary Statistical Inference (standard errors and confidence intervals, goodness-of-fit and significance tests, ANOVA and regression, and maximum likelihood and Bayesian inference). Additional topics may include Bayesian classification, ROC curves, Information theory, Fourier analysis and signal processing, Multivariate analysis, PCA and ICA, the Bootstrap, nonparametric regression, and integrate-and-fire models. Pitt Psychology TBD Pitt Neuroscience NROSCI 2012 Neurophysiology: CR HRS: 3.0 [CNBC Core Course]
This is an undergraduate neurophysiology course that will be augmented with some additional lectures for CNBC graduate students who are not neuroscientists and are not able to meet the time demands of NROSCI 2100/2101. For those students, the course satisfies the CNBC's neurophysiology core requirement; they should sign up for NROSCI 2012 (the graduate version) rather than 1012. Students in the Program in Neuroscience must take Cellular and Molecular Neurobiology (NROSCI 2100/2101, offered in the fall) instead of this course. NROSCI/MSNBIO 2102 Systems Neurobiology: CR HRS: 6.0 [CNBC Core Course]
This course incorporates neuroanatomical and neurophysiological approaches to examine the integrative functions of the brain. It consists of lectures and neuroanatomy laboratories focusing on structure/function relations using human brain specimens. The course covers in detail the major sensory, motor and behavioral regulatory systems of the brain. The course satisfies the CNBC core requirement in neuroanatomy. Pitt Mathematics MATH 3375, PSY 2480: Computational Neuroscience
Methods CR HRS: 3.0[CNBC Core Course]
Sampling of topics covered: neuron spiking; firing rates and spike statistics; reverse correlations and receptive fields; neural decoding (discrimination, population decoding, spike train decoding); electrical properties of neurons; single-compartment models; modeling channels and synpatic conductances; the Hodgkin-Huxley model; the cable equation; conductances and morphology; levels of modeling; network models (firint rate models, feedforward and recurrent networks, excitatory-inhibitory networks, stochasic networks); plasticity and learning; supervised and unsupervised learning. MATH 3950: Nonlinear Dynamics CR HRS: 3.0
Math 3950 will provide a graduate-level introduction to the area of nonlinear dynamical systems. Topics will include an introduction to phase spaces, invariant manifold theorems, reduction to center manifolds, bifurcation theory, the method of averaging, Melnikov's method, and an introduction to chaos. The approach will combine proofs with consideration of examples. The reference text for the course will be the second edition of Ferdinand Verhulst's "Nonlinear Differential Equations and Dynamical Systems"; however, much added material will be included from other sources. This course should be accessible to any student who is not put off by a few notions from analysis (convergence properties of sequences, compactness, continuity and differentiability, at the advanced undergraduate level) and has a firm grasp of undergraduate level linear algebra.
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