Fall 2018

First day of classes: Monday, August 27, 2018

CNBC Core courses:

Advanced Cellular Neuroscience, Cellular & Molecular Neurobiology, Cognitive Neuroscience,

Introduction to Parallel Distributed Processing

Note: students in the CNBC graduate training program automatically have permission to attend the core courses listed above, but cross-registration procedures may apply.

CMU Biological Sciences

03-762 Advanced Cellular Neuroscience: 12 Units [CNBC core course]

  • Instructor: Aryn Gittis
  • Days/Times: T/R 9:00AM – 10:20AM (Doherty Hall 2302), Section A: R 3:30PM – 4:50PM (MI 130), Section B: R 4:00-5:20 (TBA)

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.


86-631 Neural Data Analysis: 9 units
(Cross listed as 42-631)

  • Instructor: Steve Chase
  • Date/Time: T/R 1:30 PM – 2:50 PM
  • Location: MI 130

The vast majority of behaviorally relevant information is transmitted through the brain by neurons as trains of actions potentials. How can we understand the information being transmitted? This class will cover the basic engineering and statistical tools in common use for analyzing neural spike train data, with an emphasis on hands-on application. Topics will include neural spike train statistics, estimation theory (MLE, MAP), signal detection theory (d-prime, ROC analysis), information theory (entropy, mutual information, neural coding theories, spike-distance metrics), discrete classification (naïve Bayes), continuous decoding (PVA, OLE, Kalman), and white-noise analysis. Each topic covered will be linked back to the central ideas from undergraduate probability, and each assignment will involve actual analysis of neural data, either real or simulated, using Matlab. This class is meant for upper-level undergraduates or beginning graduate students, and is geared to the engineer who wants to learn the neurophysiologist’s toolbox and the neurophysiologist who wants to learn new tools. This course leads naturally into 42/18-632, Neural Signal Processing. Prerequisites: undergraduate probability (36-225/227, or its equivalent), some familiarity with linear algebra, and Matlab programming.

CMU Machine Learning

10-701 Introduction to Machine Learning: 12 units

  • Instructors: Ziv Bar-Joseph & Pradeep Ravikumar
  • Location: Wean Hall 7500
  • Days/Times: M/W 3:00PM – 4:20PM

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 learn 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 masters 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. If you are unsure whether you have sufficient mathematical background to do well in this course, you should consider taking the mini 10-600 Mathematical Background for Machine Learning. 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

CMU Psychology

85-707 Neuroscience of Concepts: 9 units

  • Instructor: Brad Mahon
  • Location: Baker Hall 342F
  • Days/Times: M/W 3:00PM-4:20PM

Conceptual knowledge underpins all aspects of everyday experience, from language, to thinking, to recognizing familiar objects, people and places. This seminar will survey major theories and findings about how the brain represents meaning. The course will emphasize research using neuropsychological methods in brain-damaged patients and functional neuroimaging in healthy participants. Students will read primary empirical and theoretical review articles to develop an understanding of both classic findings and recent discoveries about how the human brain represents meaning.

85-714 Cognitive Neuropsychology : 9 units

  • Instructor: Marlene Behrmann
  • Location: Baker Hall 336B
  • Days/Times: M/W 10:30AM – 11:50AM

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
  • Location: Baker Hall 340A
  • Days/Times: M/W 1:30PM – 2:50PM

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.

85-735 Neural and Cognitive Models of Adaptive Decision : 12 units

  • Instructor: Tim Verstynen
  • Location: Baker Hall 336B
  • Days/Times: T/R 3:00PM-4:20PM

Humans and other mammals exhibit a high degree of control when selecting actions in noisy contexts, quickly adapting to unexpected outcomes in order to better exploit opportunities arising in the future. This course will explore both the cognitive and neurobiological systems of adaptive decision-making, through a mixture of readings, lectures, and hands-on modeling projects (in Python and Matlab).

85-765 Cognitive Neuroscience: 12 units [CNBC core course]
Cross-listed as Pitt Neuroscience NROSCI 2005.

  • Instructor: Carl Olson
  • Location: MI 130
  • Days/Times: T/R 10:30AM – 11:50AM

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, i.e. computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans and single-neuron recording in animals. Lecture format will be used for most sessions, with a few sessions devoted to discussion.

Special permission is required: Graduate Students, instructors permission from Carl Olson at colson@cnbc.cmu.edu and once you have instructor’s permission, please see Erin Donahoe , in BH 342 E or donahoe@andrew.cmu.edu to register you.

85-770 Perception: 12 units

  • Instructor: Roberta Klatzky
  • Location: Baker Hall 336B
  • Days/Times: T/R 9:00AM – 10:20AM

Perception, broadly defined, is the construction of a representation of the external world for purposes of thinking and acting. Although we often think of perception as the processing of inputs to the sense organs, the world conveyed by the senses is ambiguous, and cognitive and sensory systems interact to interpret it. In this course, we will examine the sensory-level mechanisms involved in perception by various sensory modalities, including vision, audition, and touch. We will learn how sensory coding interacts with top-down processing based on context and prior knowledge and how perception changes with learning and development. We will look at methods of psychophysics, neuroscience, and cognitive psychology. The goals include not only imparting basic knowledge about perception but also providing new insights into everyday experiences.

85-790 Human Memory: 12 units

  • Instructor: Lynne Reder
  • Location: Baker Hall 340A
  • Days/Times: M/W 1:30PM – 2:50PM

Without memory, people would barely be able to function: we could not communicate because we would not remember meanings of words, nor what anyone said to us; we could have no friends because everyone would be a stranger (no memory of meeting anyone); we could have no sense of self because we could not remember anything about ourselves either; we could not predict anything about the future because we would have no recollections of the past; we would not know how to get around, because we would have no knowledge of the environment. This course will discuss issues related to memory at all levels: the sensory registers, i.e., how we perceive things; working and short-term memory; long-term memory or our knowledge base. We will discuss recent advances in cognitive neuroscience as they inform our understanding of how human memory works. We will discuss the differences between procedural/skill knowledge, and declarative/fact knowledge and between implicit (memories that affect behavior without conscious awareness) and explicit memory (intentional or conscious recollections). Other topics will include clinical cases of memory problems such as various forms of amnesia. GRADUATE STUDENTS ONLY.

CMU Robotics

16-720 Computer Vision: 12 units

  • Instructor: John Galeotti, Kris Kitani, Simon Lucey
  • Location: Hamerschlag Hall B131 (A), Doherty Hall 2210 (B)
  • Days/Times: Section A: M/W 12:00PM – 1:20PM, Section B: T/R 4:30PM – 5:50PM

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, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and final project. The homeworks involve considerable Matlab programming exercises.

Texts recommended, and not required.

Title: “Computer Vision Algorithms and Applications”
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

36-707 Regression Analysis: 12 units

  • Instructor: Valerie Ventura
  • Location: Scaife Hall 208
  • Days/Times: M/W 1:30PM – 2:50PM

This is a course in data analysis. Topics covered include: Simple and multiple linear regression, causation, weighted least-squares, global and case diagnostics, robust regression, exponential families, logistic regression and generalized linear models; Model selection: prediction risk, bias-variance tradeoff, risk estimation, model search, ridge regression and lasso, stepwise regression, maybe boosting; Smoothing and nonparametric regression: linear smoothers, kernels, local regression, penalized regression, regularization and splines, wavelets, variance estimation, confidence bands, local likelihood, additive models; Classification: parametric and nonparametric regression, LDA, QDA, trees. Practice in data analysis is obtained through course projects. This course is primarily for first year PhD students in Statistics Data Science; it requires an appropriate background for entering that program.

36-749 Experimental Design for Behavioral and Social Sciences: 12 units

  • Instructor: Olga Chilina, Darlene Stangl
  • Days/Times/Location: Lecture T 12:00PM – 1:20PM (Posner Hall Mellon Auditorium A35). Section A: R 12:00PM – 1:20PM (Baker Hall 140C), Section B: R 12:00PM – 1:20PM (Baker Hall 140F), Section C:  R 1:30 – 2:50 PM (Baker Hall 140E)  and Section D: F 12:00PM – 1:20PM (Wean Hall 5201), Section E: F 1:30 – 2:50 PM (Baker Hall 140F)

Statistical aspects of the design and analysis of planned experiments are studied in this course. A clear statement of the experimental factors will be emphasized. The design aspect will concentrate on choice of models, sample size and order of experimentation. The analysis phase will cover data collection and computation, especially analysis of variance, and will stress the interpretation of results. In addition to weekly lecture, students will attend a computer lab once a week. Prerequisite: 36-202, 36-220, or 36-247

Pitt Bioengineering

BIOENG 2186 Neural Engineering CR HRS: 3.0

  • Instructor: Aaron Batista & Neeraj Gandhi
  • Location: Benedum Hall G24
  • Days/Times: M/W/F 1:00PM – 1:50PM

Neural Engineering is an emerging discipline that seeks first, to understand brain function using computational and engineering principles; second, to improve health through nervous system interventions; and third, to discover principles of biological information processing that can improve computing technologies. Students will learn the principles of neuroscience and the computational tools needed for original research in neural engineering. They will develop the ability to critically evaluate scientific evidence. They will design novel experiments and approaches in neuroscience and neural engineering.

BIOENG 2615 Introduction to  Neural Engineering CR HRS: 3.0

  • Instructor: Alberto Vazquez, Bistra Iordanova, Takashi Kozai
  • Location: Benedum Hall 320
  • Days/Times: M/W 3:00PM – 4:15PM


Pitt Interdisciplinary Biomed Grad Program

INTBP 2100 Biology of Vision CR HRS: 3.0

  • Course Coordinators: Ian Sigal, Matt Smith
  • Location: TBA
  • Days/Times: M/W/F 11:00AM – 11:50AM

‘Biology of Vision’ (INTBP2100) will introduce students to the basic biology of vision and vision-related research. Topics include: ocular anatomy and development; structure and function of the anterior segment; immunology and diseases of the eye; retinal structure, function and disease; imaging the visual system; visual perception; eye movements. The overall goal is to give students an understanding of the full range of vision research from a variety of methodological perspectives. The course is designed primarily for graduate students and post-doctoral fellows at Pitt and CMU, but interested advanced undergraduate students may contact one of the course coordinators for permission to register.

Pitt Neuroscience

NROSCI 2005 Cognitive Neuroscience CR HRS: 3.0 [CNBC core course]
Cross-listed as CMU 85-765

  • Instructor: Carl Olson
  • Location: Mellon Institute 130
  • Days/Times: T/R 10:30AM – 11:50AM

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, i.e. computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans and single-neuron recording in animals. Lecture format will be used for most sessions, with a few sessions devoted to discussion.

Prerequisites: Permission of the instructor.

NROSCI/MSNBIO 2100 Cellular and Molecular Neurobiology 1: CR HRS: 5.0 [CNBC core course]
NROSCI/MSNBIO 2101 Cellular and Molecular Neurobiology 2: CR HRS: 3.0[CNBC core course]

  • Instructor: Laura Lillien
  • Location: Victoria Building 229
  • Days/Time: M/T/R/F 9:00AM – 10:50AM
  • Note: CNBC students must take both 2100 and 2101; the two parts are taught sequentially.

2100- This course is the first component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers protein chemistry, regulation of gene expression, nerve cell biology, signal transduction, development, and neurogenesis in a lecture format.

2101- This course is the second component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers the electrical properties of neurons, signal propagation in nerve cells, and synaptic transmission.

Prerequisites: A background in basic biology and permission of the instructor are required.

Note for CMU students: Section 2 ofthe PCHE Cross Registration Request Form provides a space for students to enroll in a primary choice (course), and a secondary choice in case the primary is not available. Please register for the NROSCI sections as your primary chioce and the MSNBIO sections as your secondary choice, so that when NROSCI fills up, the Registrar’s Office will automatically put you in the MSNBIO section without having to complete any additional paperwork.

Note for non-Neuroscience students:The 2100/2101 sequence assumes a substantial background in biology. Students who lack this background and cannot devote adequate time to background reading might prefer to take Advanced Cellular Neuroscience instead.

Pitt Psychology

PSY 2005 Statistical Analysis I / Advanced Statistics-UG: CR HRS: 3.0

  • Instructor: Elizabeth Votruba-Drzal, Laura Betancur Cortes
  • Location: Sennott Square 4125
  • Days/Times: M 1:00 PM – 3:55 PM

This course is the first of a two course sequence to provide the knowledge and skills needed to plan and conduct analyses using a uniform framework based on the general linear model. Students will learn techniques to conduct a variety of statistical tests; the appropriate interpretation of results will be emphasized. Topics include descriptive statistics, graphing data, sampling distributions, hypothesis testing (including power, effect sizes, and confidence intervals), T-tests, correlations, multiple regression, and polynomial regression. Students use SAS for statistical computations.

PSY 2471 Mapping Human Brain Connectivity: CR HRS: 3.0

  • Instructor: Walter Schneider
  • Location: Old Engineering Hall 303
  • Days/Times: T/R 4:00PM – 5:50PM

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.