Spring 2009

First day of classes: Pitt January 5, 2009; CMU January 12, 2009.

Core courses:

Intro to Parallel Distributed Processing,
Systems Neuroscience,

Systems Neurobiology

Note:  students in the CNBC graduate training program automatically have instructor permission to attend any of these core courses, but cross-registration procedures may apply.

CMU Biological Sciences

• Instructors: Justin Crowley
• Date/Time: Tues & Thurs 9:00 AM - 10:20 AM (Doherty Hall 2210), Mon 3:00 PM - 4:20 PM (MI 355)

This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.

03-815 Magnetic Resonance Imaging in Neuroscience: 9 units

• Instructors: Eric Ahrens
• Date/Time: Tues & Thu 3:00 PM - 4:20 PM
• Location: Mellon Institute 348

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.

CMU Computer Science

15-685  Computer Vision: 12 units

• Instructor: Tai-Sing Lee
• Date/Time: Tue & Thu 3:00 PM - 4:20 PM
• Location: Porter Hall 226C

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.

10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)

• Instructor: Zig Bar-Joseph
• Date/Time: Mon & Wed 10:30 AM - 11:50 AM
• Location: Wean Hall 5409

Machine learning studies the question "how can we build computer programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.

Registration for 15-781 is restricted to CSD PhD and MS students only. All others wishing to register should use the number 10-701, which is the home number for the Machine Learning course.

• Instructor: TBA
• Date/Time: Mon & Wed 10:30 AM - 11:50 AM
• Location: TBD
• Prerequisites: Special permission required, contact instructor.

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?

85-729 Cognitive Brain Imaging: 9 units

• Instructor: Marcel Just
• Date/Time: Tues 7:00 PM - 9:50 PM
• Location: Baker Hall 336B
• Prerequisites: 85211 or 85213 or 85411 or 85412 or 85414 or 85419. Special permission required, contact instructor.

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 c onsidered, 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.

85-795 Applications of Cognitive Science: 9 units

• Instructor: Roberta Klatzky
• Date/Time: Tue & Thu 9:00 AM - 10:20 AM
• Location: Baker Hall 336B
• Prerequisites: 85211 and 85310 and 85370
• Special permission required, contact instructor.

The famous psychologist George Miller once said that Psychology should "give itself away." The goal of this course is to look at cases where we have done so -- or at least tried. The course focuses on applications that are sufficiently advanced as to have made an impact outside of the research field per se. That impact can take the form of a product, a change in practice, or a legal statute. The application should have a theoretical base, as contrasted, say, with pure measurement research as in ergonomics. Examples of applications are virtual reality (in vision, hearing, and touch), cognitive tutors based on models of cognitive processing, phonologically based reading programs, latent semantic analysis applications to writing assessment, and measurses of consumers' implicit attitudes. The course will use a case-study approach that considers a set of applications in detail, while building a general understanding of what it means to move research into the applied setting. The questions to be considered include: What makes a body of theoretically based research applicable? What is the pathway from laboratory to practice? What are the barriers - economic, legal, entrenched belief or practice? The format will emphasize analysis and discussion by students.

85-803 Computational Models of Normal and Disordered Cognition: 9 units

• Instructor: Lynne Reder
• Date/Time: Tue & Thu 12:00 PM - 1:20 PM
• Location: Baker Hall 336B
• Instructor's Permission Required
  

This seminar has as its goal to share ideas about computational modeling of cognition and cognitive neuroscience. This semester we plan to include a focus on exploring ways to develop computational models of disordered cognition and emotion as well as normal cognition.  In past semesters (this seminar is held for one semester every other year), a number of faculty members, postdoctoral fellows and researchers as well as graduate students attended these seminars whether or not they were officially part of the training grant on computational modeling.  Anyone interested in participating is welcome.  Anyone who wishes to enroll for credit should contact Lynne Reder first.  If you are not enrolled but wish to attend, please email me so that you can be included on the mailing list.

• Instructor: John Galeotti

• Days/Times: Tue & Thu 10:00 AM - 11:20 AM
• Location: BENEDUM HALL B63
• Prerequisites: Permission of the instructor

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 the University of Pittsburgh. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.

CMU Statistics

36-702 Statistical Machine Learning CR: 12 units

• Instructor: Larry Wasserman, John Lafferty
• Days/Times: Mon & Wed 1:30 PM - 2:50 PM
• Location: Wean Hall 5409

  Description not currently available

Control theory has been an important tool for understanding the organization and operation of the nervous systems. This course introduces the general principles of control theory and its application in neuroscience. Topics include: signals and systems through Fourier transform; block diagrams and transfer functions, Laplace transform, state-space description, system responses; phase-lead and phase-lag compensators, PID controllers, theory of optimal control; Introduction to the brain: cortex, cerebellum, brainstem, spinal cord; oculomotor control: saccades,VOR, and smooth pursuit; arm movement control: loads, redundant DOF, learning, internal models; human postural control. 

Pitt Psychology

PSY 2476 Brain Connectivity Mapping: 3 units 

• Instructor: Walter Schneider
• Day/Time: Thur 3:00-5:30
• Location: Engineering Hall 303
• Prerequisites: Special permission required, contact instructor.
  Web link http://schneider.lrdc.pitt.edu/BCM/

This class examines new non-invasive MRI imaging techniques and results for creating a Human Connectome (map of all the brain areas their volume, functional specialization, and their interconnectivity). These novel/advanced techniques will advance the study of brain systems, disorders, development, training, and neurosurgery. We will examine the methods, and results of techniques available in Pittsburgh for imaging and quantifying anatomical and functional connectivity. This class will review the current literature on imaging based brain segmentation and tractography. Students will learn to use of modern connection tracking software that will be used in student projects. Graduate students are expected to do a Connectome mini-project using the techniques on data that will be collected during the course. The course covers the technology of structural, functional, and connectivity imaging of both grey matter and white matter. It will examine brain networks (sensory, executive, memory, affect, resting state) and cortical and sub cortical systems. For additional details see http://schneider.lrdc.pitt.edu/BCM/ . The class will meet Thursdays 3:00-5:30 PM room 303 Engineering Hall for both lectures and laboratory projects.