Spring 2016

First day of classes: Pitt January 6, 2016; CMU January 11, 2016.

Core courses:
Advanced Systems 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

03-763 Advanced Systems Neuroscience: 12 units [CNBC Core Course]

  • Instructors: Sandra Kuhlman
  • Date/Time/Location: Tues & Thurs 9:00 AM – 10:20 AM (Hamerschlag Hall B131), Thurs 4:30 PM – 5:50 PM (Mellon Institute 355)

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.

03-765 Advanced Neural Correlates of Learning and Memory: 12 units

  • Instructors: DJ Brasier & Sandra Kuhlman
  • Date/Time/Location: Tues & Thurs 10:30 – 11:50 AM
  • Location: Wean Hall 4709

This course will examine the biological substrates of learning, memory, and behavioral adaptation. The focus will be on addressing how neural circuits change during new skill acquisition and adapt to variations in the environment. An introduction to experience-dependent changes in neural structure and function, in addition to behavioral learning paradigms, will be provided. Then we will consider the ways in which specific changes in biological substrates give rise to the emergent properties that drive behavioral adaptation, followed by in depth coverage of deciphering which biological substrates constitute a lasting memory trace. Finally, the concept of age-dependent learning will be examined. Concepts and specific examples will come through reading of primary literature and some readings from advanced texts.


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 CNBC

86-717 Cognitive Neuropsychology: 9 units
(Cross-listed in CMU Psychology as 85-714)

  • Instructor: Marlene Behrmann
  • Date/Time: Tues & Thurs 1:30 – 2:50 PM
  • Location: Baker Hall 336B

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?


CMU Computer Science

15-694 Special Topic: Cognitive Robotics: 12 units

  • Instructor: David Touretzky
  • Date/Time: Mon & Wed 3:30 PM – 4:20 PM (Wean Hall 5310), 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: Tom Mitchell & Alexander Smola
  • Date/Time: Mon & Wed 10:30 – 11:50 AM
  • Location: Porter Hall 100

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, mathematics and algorithms needed to do research and applications in machine learning. 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. You can evaluate your ability to take the course via a self-assessment exam that will be made available to you after you register. If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the master’s level course on Machine Learning, 10-601.” This class may be appropriate for MS and undergrad students who are interested in the theory and algorithms behind ML. You can evaluate your ability to take the course via a self-assessment exam at: http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/Intro_ML_Self_Evaluation.pdf

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: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Hamerschlag Hall B103

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

85-712 Cognitive Modeling: 12 units

  • Instructor: John Anderson
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Baker Hall 340A

This course will be concerned with modeling of agent behavior in a range of applications from laboratory experiments on human cognition, high-performance simulations such as flight simulators, and video game environments like Unreal Tournament. The first half of the course will teach a high-level modeling language for simulating human perception, cognition, and action. The second half of the course will be a project in which students develop a simulated agent or agents for the application of their choice.

85-714 Cognitive Neuropsychology: 9 units (Cross-listed in CNBC as 86-717)

  • Instructor: Marlene Behrmann
  • Date/Time: Tues & Thurs 1:30 – 2:50 PM
  • Location: Baker Hall 336B

This course will review what has been learned of the neural bases of cognition through studies of brain-damaged patients as well as newer techniques such as brain stimulation mapping, regional metabolic and blood flow imaging, and attempt to relate these clinical and physiological data to theories of the mind cast in information-processing terms. The course will be organized into units corresponding to the traditionally-defined subfields of cognitive psychology such as perception, memory and language. In each area, we will ask: To what extent do the neurological phenomena make contact with the available cognitive theories? When they do, what are their implications for these theories (i.e., Can we confirm or disconfirm particular cognitive theories using neurological data?)? When they do not, what does this tell us about the parses of the mind imposed by the theories and methodologies of cognitive psychology and neuropsychology?

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

  • Instructor: David Plaut
  • Date/Time: Tues & Thurs 1:30 – 2:50 PM
  • Location: Baker Hall 336B

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.

85-726 Learning in Humans and Machines: 12 units

  • Instructor: Charles Kemp
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Baker Hall 340A

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

  • 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
  • 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: Deva Kannan Ramanan
  • Date/Time: Tues & Thurs 12:00 – 1:20 PM
  • Location: Doherty Hall 1212
  • 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 Methods in 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: TBA

Students will gain theoretical and practical skills in medical image analysis, including skills relevant to general image analysis. The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit ( ITK ), a popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh. In addition to image analysis, the course will include interaction with clinicians at UPMC. It is possible that a few class lectures may be videoed for public distribution. Prerequisites: Knowledge of vector calculus, basic probability, and either C++ or python. Required textbook, “Machine Vision”, ISBN: 052116981X; Optional textbook, “Insight to Images”, ISBN: 9781568812175.


CMU Statistics

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

  • Instructor: Larry Wasserman
  • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Hamerschlag Hall B103

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: Jordan Rodu
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Mellon Institute 355

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 2540 Neural Biomaterials and Tissue Engineering: 3 credits

  • Instructor: Tracy Cui
  • Days/Times: Days Fri 1:00 PM – 4:00 PM
  • Location: Benedum 226

This course is designed to introduce students to an advanced understanding of biomaterials and tissue engineering specialized in neural applications. It will review biomaterials used for neural prosthesis, drug delivery devices, and tissue engineering scaffold. The student will gain a fundamental understanding of the biocompatibility issues relevant to a variety of neural implantable devices and the current strategies to solve thse issues. Topics will include basic material science, neural tissue biocompatibilty with implant, bbb and cns drug delivery, tissue engineering and regenerative medicine for pns, tissue engineering and regenerative medicine for cns, neural electrode/tissue interface (including both simulating and recording electrodes, both peripheral and cortical neural interface). The student should have some exposure to biomaterials and tissue engineering before taking this course.

BIOE 2630 Methods in Image Analysis: 3 credits

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

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:50 AM
  • Location: TBA

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: Thackeray 525

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: Tues & Thurs 1:00 PM – 2:50 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, Fri 9:00 – 11:55am
  • Location: Cathedral G16A
  • 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 2475 Behavioral Neuroscience: CR HRS 3.0

  • Instructor: Julie Fiez
  • Day/Time: Mon 9:00 – 11:55am
  • Location: Sennott Square 4117

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.