First day of classes: Pitt January 4, 2011; CMU January 16, 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 03-362. 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.
86-595 Neural Data Analysis: 12 units
The vast majority of behaviorally relevant information is transmitted through the brain by neurons as trains of actions potentials. How can we understand the information being transmitted? This class will cover the basic engineering and statistical tools in common use for analyzing neural spike train data, with an emphasis on hands-on application. Topics may include neural spike train statistics (Poisson processes, interspike intervals, Fano factor analysis), estimation (MLE, MAP), signal detection theory (d-prime, ROC analysis, psychometric curve fitting), information theory, discrete classification, continuous decoding (PVA, OLE), and white-noise analysis. Each topic covered will be linked back to the central ideas from undergraduate probability, and each assignment will involve actual analysis of neural data, either real or simulated, using Matlab. This class is meant for upper-level undergrads or beginning graduate students, and is geared to the engineer who wants to learn the neurophysiologist’s toolbox and the neurophysiologist who wants to learn new tools. Those looking for broader neuroscience application (eg, fMRI) or more focus on regression analysis are encouraged to take 36-746. Those looking for more advanced techniques are encouraged to take 18-699. Prerequisites: undergraduate probability (36-225/227, or its equivalent), some familiarity with linear algebra and Matlab programming
86-675 Computational Perception: 12 units
TIn this course, we will first cover the biological and psychological foundational knowledge of biological perceptual systems, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. The course will focus on vision this year, but will also touch upon other sensory modalities. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. Topics include perceptual representation and inference, perceptual organization, perceptual constancy, object recognition, learning and scene analysis. Prerequisites: First year college calculus, some basic knowledge of linear algebra and probability and some programming experience are desirable.
86-711 Consciousness: 12 units
This is a graduate level course investigating the philosophical and empirical investigation of phenomenal consciousness. We begin with general philosophical issues about what phenomenal consciousness is, what would count as an adequate explanation of it, and why consciousness might outstrip attempts at scientific explanation. We consider in this context the nature of scientific explanation, the role of thought experiments and imagination, the psychology of how we think about consciousness. We then move to more general theories of consciousness including philosophical, computational and informational theories. We’ll consider introspection and evidence for consciousness and the challenges raised by the role of attention. Finally, we will look at neurobiological approaches to conscious phenomena in the perceptual and motor/action domains. The class meets only once a week for 2.5 hours with a sizeable break. This is to foster sustained and focused discussion. The course is restricted to CNBC graduate students. Graduate students from other departments should speak to the instructor for permission to register.
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.
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.
85-708 Visual Cognition: 9 units
Recognizing an object, face or word is a complex process which is mastered with little effort by humans. This course adopts a three-pronged approach, drawing on psychological, neural and computational models to explore a range of topics including early vision, visual attention, face recognition, reading, object recognition, and visual imagery. The course will take a seminar format.
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 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.
85-726 Learning in Humans and Machines: 12 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: 12 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.
85-756 Music and Mind: The Cognitive Neuroscience of Sound: 9 units
This course will take a multidisciplinary approach to understand the neural systems that contribute to auditory perception and cognition, using music and speech as domains of inquiry. Students will master topics in acoustics, psychophysics, cognitive psychology, cognitive development, neurophysiology, and neuropsychology. The early part of the course will provide students with a common foundation in acoustics, signal processing, and auditory neuroscience. Later in the semester, the focus will turn to developing analytical skills through critical evaluation of primary-source experimental literature. Hands-on laboratories and homework sets in sound manipulation and experimentation also will constitute a means of learning about auditory cognitive neuroscience. Throughout, the focus will be upon understanding general cognitive and perceptual challenges in perceiving and producing complex sounds like speech and music. Topics may include biological vs. cultural influences, development in infancy, perception versus production, time perception, effects of experience on perceptual processing, comparative studies of animals, attention, development of expertise, effects of brain damage, and emotional expression. Topics will be addressed from the perspective of cognitive neuroscience, in that we will attempt to understand the neural processes that give rise to auditory perception and cognition.
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.
16-725 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.
CMU Social & Decision Sciences
88-721 Emotion: Physiology, Neurobiology, Expression and Decision Making: 12 units
This course examines advanced topics in emotion from a psychological perspective. Emotions are thought to relate to numerous response channels including physiology, neurobiology and expression (facial and vocal), and each of these components and their relationships will be examined. Class will center around discussion of primary sources. This is a joint undergraduate/graduate course, students will be enrolled only with the permission of the instructor.
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, emphasizing motivation from underlying principles and interpretation in the context of neuroscience and psychology. Though 36-746 assumes only passing familiarity with school-level statistics, it moves faster than typical university-level first courses. Vectors and matrices will be used frequently, as will basic calculus. Topics include Probability, Random Variables, and Important Distributions (binomial, Poisson, and normal distributions; the Law of Large Numbers and the Central Limit Theorem); Estimation and Uncertainty (standard errors and confidence intervals; the bootstrap); Principles of Estimation (mean squared error; maximum likelihood); Models, Hypotheses, and Statistical Significance (goodness-of-fit, p-values; power); General methods for testing hypotheses (permutation, bootstrap, and likelihood ratio tests); Linear Regression (simple linear regression and multiple linear regression); Analysis of Variance (one-way and two-way designs; multiple comparisons); Generalized Linear and Nonlinear Regression (logistic and Poisson regression; generalized linear models); and Nonparametric regression (smoothing scatterplots; smoothing histograms).
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.
BIOENG 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.
BIOENG 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.
Pitt History & Philosophy of Science
HPS 2663 Perception: CR HRS 3.0
This course examines recent work in the philosophy of perception concerning ecological and relationist accounts of colour and colour perception (e.g. Thompson 1995, Matthen 2005, Cohen 2009, Aikins ms, Chirimuuta ms). These ideas have partly been inspired by psychology, neuroscience and comparative colour science, so we will discuss the empirical basis for the relationist theory, and also examine the relative merits of colour relationism in comparison to non-relationist views such as physicalism (Byrne and Hilbert 2003) and eliminativism (Hardin 1993).
Math 3370 Mathematical Neuroscience: CR HRS 3.0
This course will introduce the student to the modeling, analysis, and interpretation of large scale networks of model spiking neurons. Topics to be introduced: the statistical description of spiking dynamics, synchronous and asynchronous network dynamics, genesis of population rhythms, balanced excitatory and inhibitory networks, and internally generated population variability. Mathematical techniques include mean-field analysis, stochastic dynamics, and computational modeling. No neuroscience knowledge is assumed, but a familiarity with MATLAB, Python, or C is an asset.
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 2575 Topics in Psychology: Mapping Human Brain Connectivity: CR HRS 3.0
This class that 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 and staff interested in mapping/quantifying anatomical connections of the human brain. These techniques are used to the study of brain: systems, disorders, development, and neurosurgery planning. It will involve an optional 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. For more details go to http://www.lrdc.pitt.edu/schneider/D11.pdf For papers and images using HDFT go to http://schneiderlab.lrdc.pitt.edu/
PSY 2576 Topics Seminar in Health Psychology: 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.