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Spring 2004 First day of classes: Pitt January 5, 2004; CMU January 12, 2004. Core courses:
CMU Biological Sciences 03-315 Introduction to Magnetic Resonance Imaging in Neuroscience: 9 units
The course is designed to introduce students to 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, functional MRI (fMRI), and MR spectroscopy (MRS). Approximately, one third of the course will be devoted to introductory concepts of magnetic resonance, another third to the discussion of MRI methods, and the remaining third will cover a broad range of neuroscience applications. Guest lectures will be incorporated into the course from neuroscientists and psychologists who use MRI in their own research. 03-760/NROSCI 3059 Neural Plasticity in Sensory and Motor Systems: 9 units
Each course meeting will center around the discussion of classic and recent papers in the area of neuronal plasticity. Topics covered in the course will include 1) Basic mechanisms of synaptic plasticity 2) Developmental specification and plasticity 3) Activity dependent regulation of connectivity and circuitry 4) Mechanisms of adult plasticity. If you are interested in finding out more, or in registering for the course, contact Nathan Urban. CMU Computer Science 15-385/685 Computer Vision: 9/12 units
This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision, but biological and psychological perspectives will also be considered. Topics covered include image formation and representation, multi-scale analysis, segmentation, contour and region analysis, reconstruction of depth based on stereo, texture shading and motion, and analysis and recognition of objects and scenes using statistical and model-based techniques. 15-485/785 Computational Perception and Scene Analysis: 12 units
The goal of this course is to teach how to reason scientifically about problems and issues in perceptual cognition, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. The 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. Both the experimental approaches of scientific disciplines and the computational approaches of engineering disciplines are emphasized. Each topic in the course begins by studying the ethology of natural behaviors, analyzing and decomposing these to identify the essential components that are required for the total behavior in a natural environment. This aspect of the course follows the lines of scientific reasoning and key experiemental results that lead to our current understanding of the important computational problems in perception and scene analysis. The course then surveys the most important solutions to these problems, focusing on the idealizations and simplifications that are sensory coding, perceptual invariance, spatial vision and sound localization, visual and auditory scene segmentation, many aspects of attention, and the basics of objects and speech recognition. 15-496/782: Special Topic: Artificial Neural Networks: 12 units
Artificial neural networks combine ideas from machine learning, statistics, and pattern recognition. They draw inspiration from, and provide simplified formalizations of, theories about the workings of the brain. This course offers an introduction to neural networks for computer scientists and engineers. Prerequisites are undergraduate calculus and linear algebra, and solid programming skills. An undergraduate course in artificial intelligence or machine learning would provide helpful background but is not required. The course provides hands-on experience with a variety of neural network architectures implemented in MATLAB, and an in-depth look at problems in pattern recognition and knowledge representation. Topics covered include perceptrons, the LMS learning rule, fundamentals of pattern recognition, backpropagation learning, forward and inverse models in control theory, competitive learning, self-organizing feature maps, radial basis functions, the EM algorithm, Hopfield networks, Boltzmann machines, Helmholtz machines, and general recurrent networks. CMU Psychology 85-712/412 Cognitive Modeling: 9 units
This course will be concerned with modeling of cognition. We will use a high-level modeling language to simulate a range of cognitive tasks from the literature on attention, memory, problem solving and skill acquisition. Students will end the course developing a model for a phenomenon of their choosing. The course grade will be determined by a series of assignments involving developing cognitive models and by a written exam. 85-714/414 Cognitive Neuropsychology: 9 units
This course will review what has been learned of the neural bases of cognition through studies of brain-damaged patients as well as newer techniques such as brain stimulation mapping, regional metabolic and blood flow imaging, and attempt to relate these clinical and physiological data to theories of the mind cast in information-processing terms. The course will be organized into units corresponding to the traditionally-defined subfields of cognitive psychology such as perception, memory and language. In each area, we will ask: To what extent do the neurological phenomena make contact with the available cognitive theories? When they do, what are their implications for these theories (i.e., Can we confirm or disconfirm particular cognitive theories using neurological data?)? When they do not, what does this tell us about the parses of the mind imposed by the theories and methodologies of cognitive psychology and neuropsychology? 85-719/419 Introduction to Parallel Distributed Processing: 9 units [CNBC Core Course]
This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representation, and learning. The course will consist of lectures describing the theory behind the models as well as their implementation, and students will get hands-on experience running existing simulation models on workstations. 85-721/421 Language & Thought: 9 units
This course allows the student to explore ways in which the mind shapes language and language shapes the mind. Why are humans the only species with a full linguistic system? Some of the questions to be explored are: What kinds of mental abilities allow the child to learn language? What are the cognitive abilities needed to support the production and comprehension of sentences in real time? How do these abilities differ between people? Are there universal limits on the ways in which languages differ? Where do these limitations come from cognition in general or the specific language facility? Why is it so hard to learn a second language? Are there important links between language change and cultural change that point to links between language and culture? 85-729/429 Cognitive Brain Imaging: 9 units
This seminar will examine how the brain executes higher level cognitive processes, such as problem-solving, language comprehension, and visual thinking. The topic will be addressed by examining what recent brain imaging studies can tell us about these various kinds of thinking. This new scientific approach has the potential of providing important information about how the brain thinks, indicating not only what parts perform what function, but also how the activity of different parts of the brain are organized to perform some thinking task, and how various neurological diseases (e.g. aphasia, Alzheimer's) affect brain activity. A variety of different types of thinking will be examined, including short-term working memory storage and computation, problem solving, language comprehension, visual thinking. Several different technologies for measuring brain activity (e.g. PET and functional MRI and also some PET imaging) will be considered, attempting to relate brain physiology to cognitive functioning. The course will examine brain imaging in normal subjects and in people with various kinds of brain damage. Graduate Students Only. 85-757/457 Neural Bases of Emotion: 9 units
The goal of this course is to provide students with an overview of current perspectives about the neural bases of emotion. Special attention will be devoted to the brain systems that process reinforcers (i.e., rewards and punishments), as these are at the center of many neurally-based theories of emotion. The course will include the neuroanatomy, neurophysiology, and neurochemistry of the main brain areas implicated in emotion, as well as a look at what happens when the normal function of these areas is compromised (whether because of brain injury or mental illness). We will be making extensive use of reviews by leading experts in the field and other primary research literature, so at the end of the course students will have a good understanding of several of the current issues in the field. Some basic knowledge of neuroscience, at the level taught in 85-219, is required. Students who have not taken 85-219 but are able to show evidence of a basic understanding of neuroscience may also be allowed to register. In all cases, permission of the instructor is required. 85-767/467 Human Causal Reasoning: 9 units
This course will survey experimental investigations of human causal reasoning as well as qualitative, computational and mathematical theories that have been developed to address causal reasoning. The material covered requires the ability to grasp mathematical concepts. Course meetings will include lectures, student-led presentations, and seminar-style discussions. Evaluation will be based on two short papers and one longer final paper. CMU Robotics 16-725 Methods in Medical Image Analysis: 12 units
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 PSY 2455 Human Cognition: Language CR HRS: 2.0
Course Description not currently available. Pitt Neuroscience NROSCI 2012 Neurophysiology: CR HRS: 3.0 [CNBC Core Course]
In this course we will examine the functioning of neurons and synapses, the basic units responsible for fast communication within the nervous system. The course will focus on the elegant use of electrical mechanisms by the nervous system, and on the powerful quantitative approach to scientific investigation that is fundamental to neurophysiology. Topics that will be addressed include: principles of electric current flow exploited by the nervous system; the basis of the resting potential of neurons; the structure and function of voltage-gated and neurotransmitter-gated ion channels; generation and propagation of action potentials; the physiology of fast synaptic communication.
NROSCI/MSNBIO 2102 Systems Neurobiology: CR HRS: 6.0 [CNBC Core Course]
This course is a component of the introductory graduate sequence designed to provide an overview of neuroscience. This course provides an introduction to the structure of the mammalian nervous system and to the functional organization of sensory systems, motor systems, regulatory systems, and systems involved in higher brain functions. It is taught primarily in a lecture format with some laboratory work. 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. |