First day of classes: Pitt January 7, 2013; CMU January 14, 2013.
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 03-362. Students will attend the same lectures as the students in 03-362, plus an additional once weekly meeting. In this meeting topics covered in the lectures are 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 including work by Hodgkin and Huxley on action potentials and by Katz and Eccles on synaptic transmission. Generation and use of genetically modified animals also will be discussed. Performance in this portion of the class will be assessed by supplemental exam questions.
Course description for 03-362 (not a CNBC core course, but a component of 03-762):
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 cellular and molecular neuroscience ranging from molecules to simple neural circuits. Topics covered will include the properties of biological membranes, the electrical properties of neurons, neural communication and synaptic transmission, mechanisms of brain plasticity and the analysis of simple neural circuits. In addition to providing information the lectures will describe how discoveries were made and will develop students’ abilities to design experiments and interpret data.
CMU Biomedical Engineering
42-632 Neural Signal Processing: 12 units
(Cross-listed in Electrical & Computer Engineering as 18-698)
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 roughly 1011 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; 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.
86-601 Topics in Motor Control: 3 units
This course will delve into the literature on the neural control of movement, to gain a deep understanding of how movements are planned, coordinated, and executed. Our goal will be to synthesize the major research findings, by sifting out and summarizing the data that support various theories of motor control. Topics to be covered include representation (muscles vs. movements, reference frames), the role of feedback circuitry (basal ganglia, cerebellum), and computational frameworks (internal models, optimal control). The first class will be an organizational meeting; class time may change to accommodate conflicting schedules.
86-701 Special Topics in Cognitive Science: Attention: 9 units
There has been a resurgence of interest in attention among philosophers, and for good reason. Attention seems to be implicated in all aspects of mind. In this seminar, we shall explore the importance of attention for a variety of philosophical debates. While our focus will be on philosophical issues, we shall also examine relevant aspects of the vast empirical literature. We’ll discuss conceptual issues in the empirical theory of attention with focus on historical paradigms and current theories in psychology and neuroscience, the metaphysics of attention, as well as the role of attention in agency, memory, introspection, reference and thought, and different aspects of consciousness. This course is restricted to graduate students. While the course focuses on the relation between attention and philosophical issues, graduates students in psychology and neuroscience who are interested in conceptual issues should find this course useful as well.
CMU Computer Science
15-694 Special Topic: Cognitive Robotics: 12 units
Cognitive robotics is a new approach to robot programming based on high level primitives for perception and action. These primitives draw inspiration from ideas in cognitive science combined with state of the art robotics algorithms. Students will experiment with these primitives and help develop new ones using the Tekkotsu software framework on the Calliope robot, which includes a 5 degree-of-freedom arm with gripper, a Kinect camera on a pan/tilt mount, and Ubuntu Linux on a dual-core on-board netbook. Prior robotics experience is not necessary, but strong programming skills are required.
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.
10-702 Statistical Machine Learning: 12 units
(Cross-listed in Statistics as 36-702)
This course builds on the material presented in 10-701(Machine Learning), introducing new learning methods and going more deeply into their statistical foundations and computational aspects. Applications and case studies from statistics and computing are used to illustrate each topic. Aspects of implementation and practice are also treated. A tentative list of topics to be covered includes (but is not restricted to) the following: Maximum likelihood vs. Bayesian inference; Regression, Classficiation, and Clustering; Graphical Methods, including Causal Inference; The EM Algorithm; Data Augmentation, Gibbs, and Markov Chain Monte Carlo Algorithms; Techniques for Supervised and Unsupervised Learning; Sequential Decision making and Experimental Design.
Prerequisites: 10701 and 36705
85-701 Stress, Coping and Well-Being: 12 units
This course will examine basic processes and theory about stress and coping from a psychological perspective. The first part of the course will explore topics related to the psychobiology of stress, stress measurement, and links between stress and health. The second part of the course will explore topics on mechanisms and theoretical perspectives on coping with stress. This will include a consideration of topics such as emotion regulation, self-regulation, coping with traumatic events, alternative medicine approaches, and resilience factors. This class is a small, upper level seminar that will consist of some lecture and extensive class discussion. Active class participation is required.
This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representa-tion, 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.
85-724 Hemispheric Specialization: Why, How, and What?: VAR units
The brain is divided into two hemispheres, raising a host of questions about brain organization, hemispheric specialization and laterality. Despite all the research devoted to these questions, our understanding of the behavioral significance and neural basis of laterality remains limited. This course will address the questions of ?why?, ?how? and ?what?. We will review the latest data and empirical results but will also develop a coherent theoretical perspective, moving from molecular, genetic and evolutionary considerations to cognitive and clinical factors in the understanding of one of the most fascinating phenomena in neuroscience, neuropsychology, psychiatry, neurology, and cognitive sciences. In addition to tackling a major text in the field (The Two Halves of the Brain Edited by Hugdahl and Westerhausen), we will read the latest papers in the field. The class will be almost entirely discussion-based and students will be responsible for doing the readings ahead of time and being prepared for the discussion.
85-785 Auditory Perception: Sense of Sound: 12 units
This course explores how our sense of hearing allows us to interact with the world. Students will learn about basic principles of sound, spatial sound, sound quality, hearing impairment, auditory perception, interactions with other modalities, and auditory cognition. Topics may also include musical acoustics, basic auditory physiology, sound-semantic associations, acoustic analysis, and sound-making gestures. We will consider not only simple laboratory-generated signals, but also more complex sounds such as those in our everyday environment, as well music and speech. Students will gain some in-class experience with generating sounds and analytic listening. After students reach a sophisticated level of understanding of the auditory fundamentals, they will apply their knowledge to the study of several current issues in auditory research.
85-795 Applications of Cognitive Science: 12 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.
16-685 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.
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 new v4 of 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. NEW THIS YEAR: ITKv4 includes a new simplified interface and many new features, several of which will be explored in the class. Extensive expertise with C++ and templates is no longer necessary (but still helpful). Some or all of the class lectures may also be videoed for public distribution.
Prerequisites: Knowledge of vector calculus, basic probability, and C++ or python (most lectures will use C++). Required textbook, “Machine Vision”, ISBN: 052116981X; Optional textbook, “Insight to Images”, ISBN: 9781568812175.
Statistical ideas have been part of neurophysiology and the brain sciences 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. 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 to the brain. Topics include the Hodgkin-Huxley model; integrate-and-fire neurons; balanced excitation and inhibition; the role of neural variability; firing rate and temporal coding; population coding and decoding; potential functions of oscillations and synchrony; the psychophysics of discriminability; the “magical number seven”; power laws in learning; and optimality in perception, action, and decision-making.
BIOENG 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.
MATH 3380 Mathematical Biology: CR HRS 3.0
Mathematics of cooperative behavior will be this semester’s topic. We will look at various aspects of social behavior in animals and humans. Techniques from game theory and evolutionary dynamics will be used to study the evolution of cooperative behavior. We will also look at models for the spread of rumors and fads as well as other kinds of social dynamics for humans. Collective motion of animals such as flocking and swarming will be modeled and analyzed. Prerequisites are some knowledge of differential equations (an undergrad course should be enough ) and possibly some programming knowledge (e g Matlab or Python or XPP). Grades will be based on homework and a final written project and presentation.
MSNBIO 2614 Neuropharmacology: CR HRS: 3.0
This course will examine the molecular mechanism of drug action for different classes of drugs that act on the nervous system, antidepressants, antipsychotics, drugs to relieve pain, drugs for neurological diseases, and drug abuse and addiction.
MSNBIO 2632 Advanced Neurophysiology: CR HRS: 2.0
The primary objective of this course is for students to develop critical scientific reasoning by learning to evaluate the essential components of classic research presented in well-written papers. Secondarily, students will gain a solid foundation in neurophysiology by examining, in detail, the underlying principles underlying current flow through a neuron’s membrane, the generation and propagation of action potentials and synaptic transmission at the neuromuscular junction. Course material will consist of papers from Hodgkin, Huxley, and Katz. Students will be expected to have a fundamental understanding of Donnan equilibrium and membrane physiology. Students should have a basic understanding of electrostatics, and an understanding of differential equations.
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.
NROSCI 2112 Neurobiology of Disease: CR HRS: 3.0
This course is designed to provide a survey of some of the major neurological and psychiatric disorders for the non-clinician. Each session will focus on a particular disorder and will include a patient presentation (live or by videotape), and a discussion of the etiology, epidemiology, pathophysiology, and treatment of that disorder. Participants will be asked to do some background reading each week, to prepare a short grant application on a topic of relevance to the neurobiology of disease, and to then participate in the peer review of an applications of another course participant. Reading will consist of reviews and recent research articles.
PSY 2476 Topics Seminar in Cognitive Psychology: Computational Modeling: CR HRS 1.0-4.0
This course will provide an introduction to computational modeling in cognitive psychology. The main goals of this course are to foster both a basic understanding of the different approaches to modeling and an appreciation of the practical and philosophical issues related to modeling. The first part of the course will focus on the following questions: (1) What are computational models of cognition?; (2) What are the major approaches (e.g., production systems) that are used to model cognitive processes?; (3) How are models developed and used in research?; and (4) How are models evaluated? The second part of the course will examine these issues in more depth by comparing models that have been developed to account for phenomena in specific areas of cognitive research (e.g., episodic memory). The final part of the course will consist of student-led discussions of seminal modeling papers from the students’ areas of research. Students will also complete a modeling project or write a critique/review of existing models within their area of research.
PSY 2575 Topics in Psychology: Mapping Brain Connectivity: CR HRS 3.0
This class will cover background and technical methods of mapping High Definition Fiber Tracking of brain connectivity for basic research and clinical imaging. The class is for graduate and advanced undergraduates interested in mapping/quantifying anatomical connections of the human brain. These techniques are used to study of brain: systems, disorders, development, and neurosurgery planning. It will involve a laboratory where students will learn to use advanced computation software executing research projects including: developing technical methods, mapping brain networks, or clinical analysis of data.
PSY 2576 Topics Seminar in Health Psychology: Healthy Brain 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, aerobic exercise, 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. Class discussion and presentations will be utilized.