Spring 2017

First day of classes: Pitt January 4, 2017; CMU January 17, 2017.

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 & DJ Brasier
  • Date/Time/Location: Tues & Thurs 9:00 AM – 10:20 AM (Hamerschlag Hall B103), 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 (Gates Hillman 4101), Wed 1:30 – 2:20 PM (Porter Hall A21A)

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: Porter Hall A18A

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

86-701 Agency: 9 units

  • Instructor: Wayne Wu
  • Date/Time: Wed 1:00 – 3:50 PM
  • Location: MI 115

Description TBA


CMU Computer Science

15-686 Neural Computation: 12 units

  • Instructor: Tai Sing Lee
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Gates Hillman 4211

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 (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 Electrical & Computer Engineering

18-898 Special Topics in Signal Processing: Intro to data-science with applications to clinical neural data: 12 units

  • Instructors: Pulkit Grover
  • Location: Porter Hall A18C
  • Days/Times: T/R: 10:30-11:50 AM (could change subject to student enrollment and preferences)

This course is motivated by the increasing societal need to address healthcare problems through Big Data analysis and design of novel sensing modalities. All of the neuroscience background will be introduced in the class. Students are expected to have a strong background in linear algebra and probability (18-290, 36-217 or equivalent, senior or graduate standing preferred; discuss suitability of background with the instructor). The course will introduce students to concepts in data-science through hands-on data analyses oriented towards diagnosing and treating neural disorders such as epilepsy, traumatic brain injuries, stroke, etc. The eventual goal of the course is to produce a workforce of engineers and data-scientists who can work closely with clinicians to improve healthcare of tomorrow. We will discuss various modalities that are used to acquire clinical data, both invasively and noninvasively, and challenges in design, instrumentation, and data analyses for new modalities and multi-modal data. By the end of the course, the students will acquire statistical tools for modern data analytics and algorithms for efficient analyses such as optimization for imaging and inference, and machine-learning techniques. The analytics will be presented and performed with a deep understanding of the underlying physics of neural signal propagation as well as the particular disorder. These techniques will be used to image and infer neural activity as well as diagnose neural disorders on real and simulated data. Course projects will enable students to explore problems aimed at clinically-oriented research.


CMU Machine Learning

10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)

  • Instructor: Aarti Singh & Pradeep Ravikumar
  • 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 & Ryan Tibshirani
  • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Hamerschlag Hall B103

Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming that students have taken Machine Learning (10-701) or Advanced Machine Learning (10-715), and Intermediate Statistics (36-705). The term “statistical” in the title reflects the emphasis on statistical theory and methodology. This course is mostly focused on methodology and theoretical foundations. It treats both the art of designing good learning algorithms and the science of analyzing an algorithm?s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research. Though computation is certainly a critical component of what makes a method successful, it will not receive the same central focus as methodology and theory. We will cover topics in statistical theory that are important for researchers in machine learning, including consistency, minimax estimation, and concentration of measure. We will also cover statistical topics that may not be covered in as much depth in other machine learning courses, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation.


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.

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

  • Instructor: David Plaut
  • Date/Time: Tues & Thurs 10:30 – 11:50 AM
  • Location: Porter Hall 226C

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.

85-729 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.

85-785 Auditory Perception: Sense of Sound: 12 units

  • Instructor: Lori Heller
  • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Baker Hall 340A

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

  • Instructor: Roberta Klatzky
  • Date/Time: Tues & Thurs 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.

85-814 Research Methods in Cognitive Neuroscience : 9 units

  • Instructor: John Pyles & Mike Tarr
  • Date/Time: Mon & Wed 10:30 PM – 11:50 AM
  • Location: Baker Hall 336A

This is a hands-on laboratory course designed to foster basic skills in the empirical approaches used in cognitive neuroscience research. Students will learn how to design experiments using both correlational and interference methods, learn basic analytical approaches and how to formally present empirical results. Topics will include MRI (structural and functional), electrophysiology, brain stimulation methods, neuropsychological approaches, experimental design (e.g., event-related vs. blocked trials) and basic data analysis. You must have taken 36-309 previously. A background in basic neurobiology, such as 85-219, and some experience with Matlab are encouraged but not required.


CMU Robotics

16-720 Computer Vision: 12 units

  • Instructor: Deva 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


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

Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming that students have taken Machine Learning (10-701) or Advanced Machine Learning (10-715), and Intermediate Statistics (36-705). The term “statistical” in the title reflects the emphasis on statistical theory and methodology. This course is mostly focused on methodology and theoretical foundations. It treats both the art of designing good learning algorithms and the science of analyzing an algorithm?s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research. Though computation is certainly a critical component of what makes a method successful, it will not receive the same central focus as methodology and theory. We will cover topics in statistical theory that are important for researchers in machine learning, including consistency, minimax estimation, and concentration of measure. We will also cover statistical topics that may not be covered in as much depth in other machine learning courses, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation.


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

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:45 AM
  • Location: Benedum G24

Current research topics in biomedical image analysis will be explored with an emphasis on applying geometry and statistics to image segmentation, registration, and visualization. The goal is for computers to recognize and measure anatomical structures automatically in 2d, 3d, and 4d from prior knowledge and image features. Student projects will use (and contribute to) the national library of medicine visible human toolkit, a new C++/Open GL Library of proven and experimental methods being developed by a consortium of research institutions including our own.


Pitt Neurobiology

MSNBIO 2614 Neuropharmacology: CR HRS: 3.0

  • Instructor: Michael Palladino
  • Days/Times: Tues & Thurs 1:30 PM – 3:20 PM
  • Location: BST 1395

This course will broadly review neuropharmacology and neurobiology, study monoamine, cholinergic, and GPCR biology, and explore the blood-brain barrier and its significance to neuropharmacology. The course will focus on the molecular mechanisms of a drug action for different classes of compounds including, but not limited to, antidepressants, antipsychotics, anti-epileptics, anesthetics, weight loss, stimulants, neuroprotective, addiction, pain, and migraine drugs. In addition to the formal lectures the course will emphasize critical reading of the primary literature through journal-club style discussions and cover the most recent treatment and therapeutic avenues being developed for a broad range of neurologic and psychiatric disorders. The course is ideally suited for Molecular Pharmacology and Neuroscience graduate students or any other graduate student with an interest in neurological diseases and their treatments. The course is also appropriate for pre-professional undergraduates who have completed 4 semesters of chemistry and 2 semesters of biology.

MSNBIO 2632 Advanced Neurophysiology: CR HRS: 2.0

  • Instructor: Andy Schwartz
  • Days/Times: Thurs 1:00 PM – 3:00 PM
  • Location: TBA

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, synaptic transmission at the neuromuscular junction, and sensory transduction. Course material will consist of papers from Hodgkin, Huxley, Katz, Fatt and others. Complementing the classic papers will be contemporary work on the same topic. Students will be expected to have had basic neurophysiology and be familiar with electrostatics, electric circuits and differential equations.


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: Robert Turner
  • Days/Times: Mon & Wed 9:00 – 10:20am, Fri 9:00 – 11:55am
  • Location: Victoria Hall 116/117
  • 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.


Pitt Psychology

PSY 2476 The Cognitive Neuroscience of Learning and Memory

  • Instructor: Marc Coutanche
  • Day/Time: Thurs 2:00 – 4:50 PM
  • Location: LRDC 9th Floor

In this advanced seminar, we will explore learning and memory through the field of cognitive neuroscience, in which theories and experiments draw on both neuroscience and cognitive psychology. Through reading journal papers, participating in class discussions, and some lectures, the class will address topics such as: long-term memory, working memory, perceptual learning, spatial memory, false memory, the role of sleep in consolidation, the impact of attention on learning, emotional memory, and how learning and memory changes over the lifespan. I will be introducing students to the methods of cognitive neuroscience as the course progresses, so by the end of the semester, students will also have an understanding of neuroimaging, lesion patients, neuronal recording, brain stimulation and cognitive experiments.

PSY 2575 Sleep and Circadian Rhythms in Health and Disease

  • Instructor: Kathryn Roecklein & Tica Hall
  • Day/Time: Tues 1:00 – 4:00 PM
  • Location: 4125 Sennott Square

Sleep and circadian rhythms are in the news. Reports in the lay press and best-selling books have linked sleep and circadian rhythm disturbances to almost any adverse health outcome you can think of including anxiety and depression, substance abuse and other risky behaviors, susceptibility to the common cold, accelerated aging, obesity, diabetes, cardiovascular disease, cancer, and all-cause mortality. This seminar course will examine the evidence linking sleep and circadian rhythms to health, including important epidemiological and experimental studies. We will also examine putative biological, behavioral, and social mechanisms through which sleep and circadian rhythms affect and are affected by health and disease. This seminar course is appropriate for graduate students and post-doctoral fellows in psychology, neuroscience, public health, nursing, bioengineering or other departments/schools at PITT, CMU, and other local universities with reciprocity agreements with PITT. Advanced undergraduates may also be eligible for this course, with advance permissions from Drs. Roecklein and Hall.