Spring 2014

First day of classes: Pitt January 6, 2014; CMU January 13, 2014.

Core courses:
Advanced Cellular Neuroscience, Introduction to Parallel Distributed Processing, 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

03-762 Advanced Cellular Neuroscience: 12 units [CNBC Core Course]

  • Instructors: TBA
  • Date/Time: Tues & Thurs 9:00 AM – 10:20 AM
  • Location: Mellon Institute 130

This course is an introductory graduate course in cellular neuroscience. As such it will assume little or no background but will rapidly progress to discussions of papers from the primarily literature. The structure of the course will be about half lectures and half discussions of new and classic papers from the primary literature. These discussions will be substantially led by students in the course. Topics covered will include ion channels and excitability, synaptic transmission and plasticity, molecular understanding of brain disease and cell biology of neurons. Assessment will be based on class participation, including performance on in-class presentations and a writing assignment.


CMU Biomedical Engineering

42-632 Neural Signal Processing: 12 units
(Cross-listed in Electrical & Computer Engineering as 18-698)

  • Instructor: Byron Yu
  • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Doherty Hall 1212

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 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; 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

15-686 Neural Computation: 12 units

  • Instructor: Tai Sing Lee
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: TBD

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.

15-694 Special Topic: Cognitive Robotics: 12 units

  • Instructor: David Touretzky
  • Date/Time: Mon & Wed 3:30 PM – 4:20 PM (Gates Hillman 4101), Fri 3:00 PM – 4:20 PM (Newell Simon Hall 3206)

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.)

  • Instructor: Barnabas Poczos & Aarti Singh
  • Date/Time: Tues & Thurs 1:30 PM – 2:50PM
  • Location: Wean 7500

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)

  • Instructor: Larry Wasserman
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Scaife Hall 125

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

 


CMU Psychology

85-701 Stress, Coping and Well-Being: 12 units

  • Instructor: David Creswell
  • Date/Time: Tues & Thurs 3:00 PM – 4:20 PM
  • Location: Baker Hall 336B
  • Prerequisites: Special permission required, contact instructor.

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.

85-719 Introduction to Parallel Distributed Processing: 12 units [CNBC Core Course]

  • Instructor: David Plaut
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Baker Hall 336B

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.

85-729 Cognitive Brain Imaging : 12 units

  • Instructor: Marcel Just
  • Date/Time: Tues 7:00 PM – 9:50 PM
  • Location: Baker Hall 336B
  • Prerequisites: 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 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.

 

85-795 Applications of Cognitive Science: 12 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.


CMU Robotics

16-720 Computer Vision: 12 units

  • Instructor: Srinivasa Narasimhan
  • Date/Time: Mon & Wed 10:30 AM – 11:50 AM
  • Location: Newell Simon Hall 1305
  • Prerequisites: (15122) and (21259) and (18202 or 21241)

This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry, and calibration, computational imaging, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, physics-based vision, image segmentation and object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and a final project. The homeworks involve considerable Matlab programming exercises. Texts recommended but not required: Title: “Computer Vision Algorithms and Applications” Author: Richard Szeliski Series: Texts in Computer Science Publisher: Springer ISBN: 978-1-84882-934-3 Title: “Computer Vision: A Modern Approach” Authors: David Forsyth and Jean Ponce Publisher: Prentice Hall ISBN: 0-13-085198-1

16-725 Medical Image Analysis: 12 units

(Cross-listed as Pitt Bioengineering BIOE 2630: Methods in Image Analysis.)

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:50 AM
  • Location: Gates Hillman Center 5222

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.

16-899 The Visual World as seen by Neurons & Machines: 12 units

  • Instructor: Abhinav Gupta & Elissa Aminoff
  • Days/Times: Mon & Wed 10:30 AM – 11:50 AM
  • Location: Newell Simon Hall 3002

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? Can we verify machine representation using human subjects? How can computer vision help understand human vision? What are the advantages of combining computer vision with our understanding of the neural mechanisms underlying human vision? In this course, through lectures, paper presentations, and projects, we will explore what we understand about human vision and how that can help in designing computational perception algorithms. We will also explore how assumptions underlying machine perception frameworks can be verified using fMRI studies of human brain; and, whether machine perception frameworks can provide a model to understand the cortical representation of the visual world.


CMU Social & Decision Sciences

88-355 Social Brains: Neural Bases of Social Perception and Cognition: 9 units

  • Instructor: Mina Cikara
  • Date/Time: Tues 6:30 PM – 9:20 PM
  • Location: Porter Hall A19C

By some accounts, the large expansion of the human brain evolved due to the complex demands of dealing with social others?competing or cooperating with them, deceiving or empathizing with them, understanding or misjudging them. This discussion-based seminar surveys the emerging field of social cognitive neuroscience and its multi-level approach to understanding the brain in its social context. We will review current theories and methods guiding the field and recent research examining the neural bases of social processes, including: theory of mind, empathy, emotion, morality, among others. We will also discuss broader questions that apply to the specific topics that the course covers, including: What are appropriate levels of description for the target phenomena? How can different disciplines in neuroscience and the social sciences contribute to social neuroscience research? What can we learn from animals? behavior about human social cognition? Do neural systems exist that are specialized for social cognition, or do the systems that participate in social cognition have more general cognitive functions?


CMU Statistics

36-702 Statistical Machine Learning: 12 units
(Cross-listed in Machine Learning as 10-702)

  • Instructor: Larry Wasserman
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Scaife Hall 125

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

36-746 Statistical Methods for Neuroscience and Psychology: 12 units

  • Instructor: Rob Kass
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Mellon Institute 130

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).


Pitt Bioengineering

BIOE 2630 Medical Image Analysis: 3 credits

(Cross-listed as CMU Robotics 16-725: Methods in Image Analysis.)

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:50 AM
  • Location: Gates Hillman Center 5222

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.


Pitt Mathematics

 

MATH 3370 Mathematical Neuroscience: CR HRS 3.0

  • Instructor: Bard Ermentrout
  • Day/Time: Mon, Wed, & Fri 1:00 PM – 1:50 PM
  • Location: Benedum G28

This course can be regarded as a second term of Computational Neuroscience but with an emphasis on the dynamics of neurons and networks. The book we will use is Mathematical Foundations of Neuroscience which you
can download for free from the Springer Web site if you are a Pitt student. Prerequisites are some probability theory and some differential equations. Topics are: Ion channels & their effects on dynamics (spiking, bursting, “canards”), Propagation of action potentials (traveling waves, evans functions, etc), The effects of noise on neurons and oscillators, Firing rate models (where they come from – averaging & mean field analysis), Oscillations (both regular and relaxation), and Spatial models (pattern formation, waves, synchronization and applications to pathologies).


Pitt Neurobiology

MSNBIO 2614 Neuropharmacology: CR HRS: 3.0

  • Instructor: Michael Palladino
  • Days/Times: Mon & Wed 1:30 PM – 2:55 PM
  • Location: BST 1395

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.


Pitt Neuroscience

NROSCI/MSNBIO 2102 Systems Neurobiology: CR HRS: 6.0 [CNBC Core Course]

  • Note: to register, sign up for NROSCI 2102 and list MSNBIO 2102 as second choice in case the class fills up.
  • Instructor: Dan Simons
  • Days/Times: Mon & Wed 9:00 – 10:20am (Victoria Hall 230), Fri 9:00 – 11:55am (Victoria Hall 230)
  • Prerequisites: MSNBIO 2100 OR NROSCI 2100 (Cellular and Molecular Neurobiology), or INTBP 2000 (Foundations in Biomedical Science), or permission of the instructor. A background in basic biology is required. If students have not had college biology courses, they must obtain consent of the instructor to enroll.

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.


Pitt Psychology

PSY 2471 Topics in Psychology: Mapping Brain Connectivity: CR HRS 3.0

  • Instructor: Walter Schneider
  • Day/Time: Tues 4:00 PM – 5:50PM, Thurs 4:00 PM – 4:50PM
  • Location: Old Engineering Building 303

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 2475 Behavioral Neuroscience: CR HRS 3.0

  • Instructor: Julie Fiez
  • Day/Time: Mon 9:00 AM – 12:00 PM
  • Location: TBA

Methods and data from the fields of neuroscience and cognitive neuroscience are beginning to play an increasingly important role in the development of basic theories about cognitive function, and in our understanding of clinical disorders such as depression and addiction. Many graduate students in psychology are not able to take full advantage of these related areas of study, however, because they lack prior exposure to basic biological and neuroscientific facts and methodologies. The objectives of this course are to: 1) introduce basic facts and methods of systems and cognitive neuroscience, cellular and pharmacological neuroscience, and molecular neuroscience; 2) provide a neuroscientific overview of cognitive topics (e.g., perception, language, emotion), and 3) provide a neuroscientific overview of disordered and impaired cognition (e.g, depression, Schizophrenia) The proposed course will be open to all graduate students, but it is specifically designed to be a required core course in the Clinical and Cognitive graduate training programs in the Department of Psychology.

PSY 2476 Topics Seminar in Cognitive Psychology: CR HRS 1.0-4.0

  • Instructor: Natasha Tokowicz
  • Day/Time: Wed 1:30 – 4:30 pm
  • Location: TBA

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 (research proposal) will be required.