Spring 2018

First day of classes: Pitt January 8, 2018; CMU January 16, 2018.

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)

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 systems neuroscience, a rapidly growing scientific field that seeks to link the structure and function of brain circuitry to perception and behavior. This course will explore brain systems through a combination of classical, Nobel prize-winning data and cutting edge primary literature. Topics will include sensory systems, motor function, animal behavior and human behavior in health and disease. Lectures will provide fundamental information as well as a detailed understanding of experimental designs that enabled discoveries. Finally, students will learn to interpret and critique the diverse and multimodal data that drives systems neuroscience. 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.

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

86-675 Computational Perception: 12 units

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

In 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.

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

  • Instructor: Pradeep Ravikumar & Manuela Veloso
  • 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: https://qna-app.appspot.com/view.html?aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgICgpO-KCgw ML course comparison: https://docs.google.com/document/d/1Y0Jx_tcINWQrWJx31WGEQSsUs059OUMmPIVSeyxNdeM/edit

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: Doherty Hall 1212

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: Gates Hillman 4211

This course provides an overview of Parallel-Distributed-Processing/neural-network models of perception, memory, language, knowledge representation, and learning. The course consists of lectures describing the theory behind the models as well as their implementation, and their application to specific empirical domains. Students get hands-on experience developing and running simulation models.

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. This course number is for graduate students only. Please email Dr. Charles Kemp at ckemp@andrew.cmu.edu for the instructors permission. Then, you will need to contact Erin Donahoe at donahoe@andrew.cmu.edu to register you.

85-732 Data Science for Psychology & Neuroscience : 12 units

  • Instructor: Tim Verstynen
  • Date/Time: Tues & Thurs 3:00 PM – 4:20 PM
  • Location: Baker Hall 336B

This course will cover advanced topics in statistics and experimental design necessary for applied research in modern psychology, including information design, exploratory data analysis, data visualization, nonparametric statistics, data and inference errors (multicollinearity, overfitting, Simpsons and Robinsons paradox), sanitization (data anonymization, de-identification), and linear models (including conditional process models). Students will get hands on experience with simulating, analyzing, and visualizing data in the R statistical environment. This course number is for Graduate Students only. Please email Dr. Tim Verstynen at timothyv@cmu.edu for instructors permission and CC the Graduate Manager, Erin Donahoe at donahoe@andrew.cmu.edu to register you.

85-814 Research Methods in Cognitive Neuroscience : 9 units

  • Instructor: John Pyles & Mike Tarr
  • Date/Time: Mon & Wed 3:00 PM – 4:20 PM
  • Location: Baker Hall 332P

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: Kris Kitani
  • Date/Time: Tues & Thurs 12:00 – 1:20 PM
  • Location: Baker Hall A51

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: Doherty Hall 1212

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 G27

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: TBA

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.

BIOE 2650 Learning & Control of Movement: 3 credits

  • Instructor: Gelsy Torres
  • Days/Times: Mon & Wed 3:00 PM – 4:15 PM
  • Location: Old Engineering Hall 316

The course will blend robotics, probability, and neuroscience to better understand the human motor system, particularly motor learning and control of movement. While motor control will be discussed as a feedback control problem, these theories will be compared during the entire course to what we know about the motor system. We will begin by studying muscle activation and forces, muscle sensory organs, spinal control structures, and inertial dynamics of a multi-joint limb. This will give us a sense of the machinery that the nervous system must control in order to generate coordinated movements. Probability foundations will be used as a framework to model how the nervous system updates estimates of limb position and sensory feedback during movements. Finally we will consider how disease can inform us about principles of movement control and motor learning. The course material and associated homework will require the students to use matlab to simulate control of biomechanical systems. This will allow students to appreciate the value of models to generate hypothesis and possible explain biological behaviors.

Pitt Mathematics

MATH 3370 Mathematical Neuroscience: CR HRS: 3.0

  • Instructor: Brent Doiron
  • Days/Times: Mon, Wed, & Fri 1:00 PM – 1:50 PM
  • Location: Thackeray Hall 525

Course covers computational and mathematical neuroscience. It will do modeling and analysis of complex dynamics of single neurons and large-scale networks.

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.

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 Building 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.

Pitt Psychology

PSY 2471 Mapping Human Brain Connectivity: CR HRS 3.0

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

This class will cover background and technical methods of mapping high definition fiber tracking of brain connectivity for basic research and clinical imaging. Students will learn to map/quantify anatomical connections of the human brain. These techniques are used to study brain systems, disorders, and development, and to assist in planning neurosurgery. Students may take an optional one-credit laboratory in which they will learn to use advanced computation software to execute research projects including developing technical methods, mapping brain networks, or clinical analysis of data.

PSY 2475 Behavioral Neuroscience: CR HRS 3.0

  • Instructor: Jamie Hanson
  • Day/Time: Mon 2:00 – 4:50 PM
  • Location: LRDC

This course will explore the links between behavior (normal and abnormal) and the brain. Cognitive and affective processes will be emphasized. Throughout the course, students will be exposed to research involving humans and animals at a variety of levels: e.g., Analysis of behavior, neuroimaging studies of whole brain activity, recordings from single brain cells, examinations of brain chemistry, and manipulations and study of genetic information. No prior knowledge of biology or neuroscience is required. The format of the course will include both lectures and discussions of scientific papers. The lectures will introduce basic facts and methods of cognitive, systems, cellular, and molecular neuroscience, and they will provide overviews of topics in cognitive and affective neuroscience, considering both normal and clinically-impaired behavior and brain function. The readings will provide an opportunity to consider different methods in the context of the primary literature, permit selected topics to be explored in greater depth, and provide a foundation for self-exploration and evaluation of cognitive and affective neuroscience literatures.