First day of classes: Pitt January 5, 2011; CMU January 10, 2011.
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
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.
Course description for 03-363 (not a CNBC core course, but a component of 03-763):
Modern neuroscience is an interdisciplinary field that seeks to understand the function of the brain and nervous system. This course provides a comprehensive survey of systems neuroscience, a rapidly growing scientific field that seeks to link the structure and function of brain circuitry to perception and behavior. This course will explore brain systems through a combination of classical, Nobel prize-winning data and cutting edge primary literature. Topics will include sensory systems, motor function, animal behavior and human behavior in health and disease. Lectures will provide fundamental information as well as a detailed understanding of experimental designs that enabled discoveries. Finally, students will learn to interpret and critique the diverse and multimodal data that drives systems neuroscience.
86-710 Philosophical and Conceptual Foundations of Cognitive Science: 12 units
This course examines the conceptual foundations that guide research on the mind/brain. Topics will vary each year but may include (non-exhaustive list): modularity, innateness, automaticity and control, neural coding and semantics, spatial representation and reference frames, attention, the nature of neurobiological and psychological explanations, memory systems. Discussion will be based on theoretical and empirical papers. A portion of the course will focus on how empirical work addresses longstanding philosophical problems including morality, agency, the will, and consciousness, topics being increasingly subjected to empirical approaches. Lecture/Discussion will be twice a week. Requirements will be several short writing assignments, namely student position papers on the topics at issue. This is a graduate course, but advanced undergraduates with a strong background in neuroscience, psychology, and or philosophy admitted with instructor permission.
CMU Computer Science
15-685 Computer Vision: 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.
15-686 Neural Computation: 12 units
(Cross-listed as 86-686)
Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities — such as perception, language, motor control, and learning — by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.
CMU Machine Learning
10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)
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.
85-712 Cognitive Modeling: 12 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?
85-729 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 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
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.
16-725 Medical Image Analysis: 12 units
(Cross-listed as Pitt Bioengineering BIOE 2630: Methods in Image Analysis.)
Students will gain theoretical and practical skills in medical image analysis, including skills relevant to general image analysis. 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 popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh. In addition to image analysis, the course will include interaction with clinicians at UPMC.
Prerequisites: Knowledge of C++ with templates, vector calculus and basic probability. Required textbook, “Machine Vision”, ISBN: 9780521830461; Optional textbook, “Insight to Images”, ISBN: 9781568812175.
16-899A Pixels to Percepts: Visual Perception for Computer Vision and Graphics: 12 units
Why do things look the way they do? Why is understanding the visual world, while so effortless for humans, so excruciatingly difficult for computers? What insights from the human visual system can we use in computer vision? What quirks of visual perception can we exploit in computer graphics? In this seminar course, through lectures, paper presentations, and projects, we will explore a number of familiar yet mysterious perceptual phenomena that involve color, illumination and shadows, material and object appearance, scenes, movement etc., both in terms of understanding (computer vision) as well as modeling (computer graphics). Basic techniques for designing psychophysical experiments will also be presented.
Prerequisites: any Computer Vision or Computer Graphics class
36-702 Statistical Machine Learning: 12 units
Description not currently available
36-759 Statistical Models of the Brain: 12 units
This new course is intended for CNBC students, as an additional option for fulfilling the computational core course requirement, but it will also be open to Statistics and Machine Learning students. It should be of interest to anyone wishing to see the way statistical ideas play out within the brain sciences, and it will provide a series of case studies on the role of stochastic models in scientific investigation. Statistical ideas have been part of neurophysiology and the brainsciences since the first stochastic description of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago. Many contemporary theories of neural system behavior are built with statistical models. For example, integrate-and-fire neurons are usually assumed to be driven in part by stochastic noise; the role of spike timing involves the distinction between Poisson and non-Poisson neurons; and oscillations are characterized by decomposing variation into frequency-based components. In the visual system, V1 simple cells are often described using linear-nonlinear Poisson models; in the motor system, neural response may involve direction tuning; and CA1 hippocampal receptive field plasticity has been characterized using dynamic place models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities; and some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models incorporating random “noise” components are used as an effective vehicle for data analysis. In neuroscience, however, the models also help form a conceptual framework for understanding neural function. This course will examine some of the most important methods and claims that have come from applying statistical thinking.
BIOE 2540 Neural Biomaterials and Tissue Engineering CR HRS: 3.0
This course is designed to acquaint students with a understanding of biomaterials and biocompatibility of various neural implants while also discussing current approaches and theories in neural tissue engineering research.
BIOE 2696 Control Theory in Neuroscience CR HRS: 4.0
Control theory has been an important tool for understanding the organization and operation of nervous systems. This course introduces the general principles of control theory and its applications 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’s motor systems: cortex, cerebellum, brainstem, spinal cord; oculomotor control: saccades, VOR, and smooth pursuit; arm movement control: loads, redundant DOF, learning, internal models; human postural control.
BIOE 2800 Neurotechnology: Concepts, Patients and Devices CR HRS: 3.0
This survey course will introduce students to biomedical devices that interface with the nervous system. Lectures within the course will fall under three primary categories: Science & Technology, Patients, and Devices. In the Science & Technology section, Pitt and CMU neuroengineers will lecture on fundamental topics in the design of neurostimulation and recording devices. During the Patients classes, clinicians will discuss the pathology, epidemiology, and current treatments for patients within their respective fields and will lead a discussion on how neurotechnology may provide benefits. In the Devices section, currently available and future neurotechnology devices will be reviewed by local clinicians and bioengineers. Speakers will provide several relevant journal articles prior to class, which students will be expected to read to facilitate classroom discussion. Student teams will also research and give presentations on neural devices in development outside of Pittsburgh. At the completion of this class, students will be familiar with key concepts involved in designing devices, with the types of patients that receive neural devices, and with the research and development of current and future neurotechnologies. Students will be expected to have a basic understanding of physiology and biomedical instrumentation.
Math 2940 Applied Stochastic Methods: CR HRS 3.0
The interpretation and modeling of stochastic phenomena is a critical component in many theories of physical, chemical, economical, and biological systems. This course aims to introduce the basics of probability and stochastic processes from a statistical mechanics approach, with an emphasis on practical applications. Specific course topics include:
*Review of basic random variables: Bernoulli, geometric, exponential, normal random variables
*Stochastic processes: Poisson process, Wiener process
*Discrete models: Markov chains, Master equations, Birth-death processes, Gillespie algorithm
*Continuous models: Stochastic Differential equations, Fokker-Plank equation, Linear Response analysis
*Backwards Kolmogorov Equation: First passage time equations, Escape over a potential barrier (Kramer’s rate)
A course Syllabus can be found at: http://www.math.pitt.edu/~bdoiron/Syllabus_2940.pdf
Math 3380 Mathematical Biology: CR HRS 3.0
Since the seminal book, The Geometry of Biological Time, was published in the early 1980s, much progress has been attained on the mathematical analysis of biological and other natural rhythms. This course will introduce students to the rhythmic phenomena in Nature ranging from the synchronization of populations of sheep on Scottish islands to the coordination of rhythms at the cellular level. We will start with a series of mechanisms for creating limit cycle oscillations. Then we will turn to the mathematics of synchronization and study how the interactions of the dynamics and the connectivity of active systems will affect their global behavior. While biological systems will be the main focus, we will also look at some physical examples such as Huygen’s clocks and the Millennium bridge. The prerequisites are basic differential equations and a familiarity with linear algebra.
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.
PSY 2476 Topics Seminar in Cognitive Psychology: CR HRS 1.0-4.0
In this seminar, we will discuss models of bilingualism, and the psycholinguistic aspects of being bilingual. Topics that may be covered include: bilingual memory representation as a function of language proficiency, language learning method, and word/concept type; how bilinguals recognize words (printed and spoken); whether the bilingual’s two languages are always “active” to some extent; how bilinguals manage to perform in one language without getting “mixed up”; and, how speaking two languages influences thought. Discussion will be emphasized during meeting periods, and a final paper will be required.
PSY 2576 Topics Seminar in Health Psychology: fMRI: CR HRS 2.0-3.0
This seminar will focus on both fundamental and current research employing structural and functional MRI methods. Early research on the nature of the functional MRI response and neurovascular coupling will be discussed as well as designing experiments and techniques for analyzing data. In the last half of the semester, current research that either extends MRI methodology or relates the method to a new question will be discussed. The goal of the class is for students to learn about the evolution and development of neuroimaging techniques, to understand the strengths and limitations of neuroimaging methods, and to become more comfortable with reading, interpreting, and critiquing neuroimaging manuscripts. Students with an interest in understanding neuroimaging methods, but without much background, are encouraged to attend or email the instructor for more information.