First day of classes: Pitt January 6, 2010; CMU January 11, 2010.
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.
CMU Biomedical Engineering
42-590/18-699A Special Topics in Signal Processing: Neural Signal Processing: 12 units
The brain is among the most complex systems ever studied. Underlying the brain’s ability to process sensory information and drive motor actions is a network of 10^11 neurons, each making 10^3 connections with other neurons. Modern statistical and machine learning tools are needed to interpret the plethora of neural data being collected, both for (1) furthering our understanding of how the brain works, and (2) designing biomedical devices that interface with the brain.
This course will cover a range of statistical methods and their application to neural data analysis. The statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, Bayesian inference, and spectral analysis. The neuroscience applications include neural decoding, firing rate estimation, neural system characterization, sensorimotor control, spike sorting, and field potential analysis.
Prerequisites: (18-290 or 18-396) and 36-217, or equivalent introductory probability theory and random variables course; an introductory linear algebra course; senior or graduate standing. No prior knowledge of neuroscience is needed.
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
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.
15-859B Machine Learning Theory: 12 units
This course will focus on theoretical aspects of machine learning. We will examine questions such as: What kinds of guarantees can we prove about machine learning algorithms? Can we design algorithms for interesting learning tasks with strong guarantees on accuracy and amounts of data needed? (Why) is Occam’s razor a good idea and what does that even mean? What can we say about the inherent ease or difficulty of learning problems? Can we devise models that are both amenable to theoretical analysis and make sense empirically? Addressing these questions will bring in connections to probability and statistics, online algorithms, game theory, complexity theory, information theory, cryptography, and empirical machine learning research.
Prerequisites: Either 15-781/10-701/15-681 Machine Learning, or 15-750 Algorithms, or a Theory/Algorithms background or a Machine Learning background.
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.
85-714 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?
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.
85-720 Introduction to Event Related Potentials: 9 units
This course provides an introduction to the use of EEG as a method to help understand the time course of cognitive brain function. There is a lecture/seminar and a lab associated with this course. Lectures include discussions of what is ERP, its unique contribution to helping to understand cognitive processes, and the ERP components that have been identified as markers of various cognitive processes. Classic and recent ERP papers are discussed as illustrations of these components and principles of this method. The lab portion of the course covers the neural basis of ERPs, recordings, principles of electricity, cap application, experimental design, methods for data acquisition and analyses.
85-726 Learning in Humans and Machines: 9 units
This course provides an introduction to probabilistic models of cognition. The focus is on principles that can help to explain human learning and to develop intelligent machines. Topics discussed will include categorization, causal learning, language acquisition, and inductive reasoning.
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.)
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.
36-702 Statistical Machine Learning: 12 units
Description not currently available
36-746 Statistical Methods for Neuroscience and Psychology: 12 units
This course provides a survey of basic statistical methods in neuroscience and psychology. It is different than a typical introductory statistics course partly because of its emphasis on the subject matter of neuroscience and psychology, but also because it attempts a broad overview that includes some of the underlying principles. This makes it move faster than typical first courses. Vectors and matrices will be used frequently, as will basic calculus. Some review materials on relevant math will be provided.
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: 3.0
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.
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.
Math 3370 Mathematical Neuroscience: CR HRS 3.0
This course focuses on interesting mathematical features of neuroscience models. A particular emphasis will be placed on analysis of dynamics of models of single neurons and networks.
PSY 2330 Developmental Psychology: Cognitive Development: CR HRS 3.0
This course will provide an introduction to central theories and issues in the study of cognitive development in infancy and childhood. The course will cover (a) theoretical frameworks for studying cognitive development, including constructivist, sociocultural, and dynamic systems theories; and (b) specific topics in the study of cognitive development, including object knowledge, theory of mind, language acquisition, and scientific reasoning.
PSY 2455 Language & Reading : CR HRS 3.0
This 3-credit core graduate course examines central theoretical issues and empirical results in the study of language and reading processes. These include the architecture of language processing, sentence and text comprehension, word identification, cross-language and cross writing system comparisons, and differences in reading skill. Beyond the core content, training goals are that students gain experience in reasoning about, discussing, and writing about complex issues and understand the roles played by cognitive and cognitive neuroscience research methods. Classes are based on lecture-discussion. Students prepare for discussions by reading assignments in advance of class and write two brief out-of-class essay exams.
PSY 2470 Human Cognition: Skill Acquisition: CR HRS 3.0
This course will introduce the foundational theories and issues in research on skill acquisition, problem solving, and reasoning. Core questions include: what is the nature of expert problem solving and reasoning, what changes cognitively as an individual moves from novice to expert, and what factors influence who becomes an expert and how quickly they get there? This course focuses on the skills that experts develop rather than the knowledge they have, although the interrelationship of knowledge and skill will be examined. We will also examine research methods used in this area — in other words, how human problem solving and reasoning can be studied scientifically, and why the results of experimental investigations support particular theories of human skill acquisition, problem solving, and reasoning.
PSY 2476 Mapping Brain Connectivity with High Definition Fiber Tracking: CR HRS 3.0
This course covers use of a novel Pittsburgh-developed High Definition Fiber Tracking (HDFT) technology providing human brain connectivity with unprecedented fidelity. The technology maps 250,000+ tracts per person (50 miles of tracts from within the head) from source to destination, mapping tract bundles and termination contact surfaces of the tissue (e.g., cortical mantle, thalamus, hippocampus). The fibers can be visualized and quantified allowing the first reliable quantification of degree of connectivity in many circuits. These techniques will advance the study of brain systems, disorders, development, neuropathology, and neurosurgery. Students will perform projects analyzing collected data on 4 individuals or developing new analysis methods. Students will be encouraged to work individually or in groups to do publication-level neuroanatomy or methodological development projects in the course. Sample projects might include mapping a sensory system, affect system, motor body positions, between-species agreement (human/primate), developmental assessment, or automated brain segmentation and circuit tracing. The class will meet Thursday 3-6PM in Old Engineering Hall for both lectures and laboratory projects. Students must have some statistics and research experience/course work and consent of the instructor. For additional details see http://schneider.lrdc.pitt.edu/HDFT/.
PSY 2576 Topics Seminar in Health Psychology: Health and Aging: CR HRS 2.0-3.0
This seminar will focus on current research examining health factors that affect adult development from a cognitive, social, and neuroscientific perspective. Topics will include nutrition and diet, physical activity, social support, intellectual engagement and education, and hormones and genetics. These factors will be discussed on molecular/cellular levels through cognitive/social levels in the hope of bridging research from a variety of disciplines.
Senior level undergraduates and graduate students interested in the course are encouraged to email the instructor for further information.