Class Schedule – Fall 2017

Fall 2017

First day of classes: Monday, August 28, 2017

CNBC Core courses:

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

Computational Models of Neural Systems, Statistical Models of the Brain,

Computational Neuroscience

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), T 3:30PM – 4:50PM (MI 130)

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

CMU CNBC

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 Computer Science

15-883 Computational Models of Neural Systems: 12 units [CNBC core course]

• Instructor: David Touretzky
• Date/Time: M/W 4:30 PM – 5:50 PM
• Location: GHC 4211

This course is an in-depth study of information processing in real neural systems from a computer science perspective. We will examine several brain areas, such as the hippocampus and cerebellum, where processing is sufficiently well understood that it can be discussed in terms of specific representations and algorithms. We will focus primarily on computer models of these systems, after establishing the necessary anatomical, physiological, and psychophysical context. There will be some neuroscience tutorial lectures for those with no prior background in this area.

CMU Machine Learning

10-701 Introduction to Machine Learning: 12 units

• Instructors: Ziv Bar-Joseph & Maria Balcan
• 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

85-708 Visual Cognition: 12 units

• Instructor: David Plaut
• Location: Baker Hall 336A
• Days/Times: T/R 10:30AM – 11:50AM

Recognizing an object, face or word is a complex process which is mastered with little effort by humans. This course adopts a three-pronged approach, drawing on psychological, neural and computational models to explore a range of topics including early vision, visual attention, face recognition, reading, object recognition, and visual imagery. The course will take a seminar format.

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.

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.

CMU Robotics

16-720 Computer Vision: 12 units

• Instructor: Srinivasa Narasimhan, Yaser Sheikh, Lucy Simon
• Location: Doherty Hall 2315 (A), Doherty Hall A302 (B)
• Days/Times: Section A: M/W 4:30PM – 5:50PM, 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: Porter Hall A22
• Days/Times: M/W 1:30PM – 2:20PM

This is a course in data analysis using multiple linear regression. Topics covered include simple linear regression, ordinary least squares and weighted least squares, the geometry of least squares, quadratic forms, F tests and ANOVA tables, residuals, outlier detection, and identification of influential observations, variable selection methods, and modern regression techniques. Essential background in linear algebra is reviewed where necessary. When time permits other topics such as nonlinear regression and robust estimation will be discussed. Practice in data analysis is obtained through course projects.

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

• Instructor: Olga Chilina
• Days/Times/Location: Lecture T 12:00PM – 1:20PM (Doherty Hall 2210). Section A: R 12:00PM – 1:20PM (Baker Hall 140CF), Section B: R 1:30PM – 2:50PM (Baker Hall 140F), Section C: F 12:00PM – 1:20PM (Porter Hall A21A)  and Section D: 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

36-759 Statistical Models of the Brain: 12 units  [CNBC core course] Cross-listed as Pitt MATH 3375

• Instructor: Rob Kass & Brent Doiron
• Location: Mellon Institute 130
• Days/Times: W/F 1:30PM – 2:50PM

This introductory course in computational neuroscience is intended for a broad range of CNBC students, with backgrounds that may be either technical (math, engineering, statistics, etc.) or non-technical (biology, neuroscience, etc.). The course is co-taught by Brent Doiron and Rob Kass. Pitt students should register in MATH 3375; CMU students may register in 36-759. The two instructors settled on “statistical models” as a unifying theme for the many kinds of models discussed, ranging from those that describe the physiology of neurons to those that describe human behavior. Statistical ideas have been part of neurophysiology since the first probabilistic descriptions of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago; they have been part of experimental psychology even longer. In broad stroke, this course will examine a few of the most important methods and claims that have come from applying statistical thinking to the brain. However, some of the topics involve tools typically taught in statistics courses, while other topics involve tools taught in math courses. Even at an intuitive level, a single course can not provide a comprehensive view of computational neuroscience; the field is too broad. Instead, by studying a series of examples, many of them very influential, students will come away with a sense of the way that computational methods contribute to contemporary understanding of neuroscience.

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.

Pitt History and Philosophy of Science

HPS 2541 History of Neurosciences CR HRS: 3.0

• Instructor: Mazviita Chirimuuta
• Location: Cathedral of Learning G28
• Days/Times: R 2:00PM – 4:00PM

This seminar takes a philosophically motivated inspection of the sciences of the brain and nervous system in the late 19th and early 20th century. Figures such as the neurologist John Hughlings Jackson (1835-1911) and physiologists Herman von Helmholtz (1821-1894) and Emil du Bois-Reymond (1818-1896) established an explicitly mechanistic research tradition in brain science. As noted by Thomas Henry Huxley, as early as 1874, this research had radical implications for the understanding of mental causation and the metaphysics of mind and sensations. In this course we will examine both the significant scientific discoveries and the philosophical debates that attended their dissemination amongst the learned public. We will consider their legacy in shaping the trajectory of philosophy of mind in the 20th century. We will also examine the steps that were made in this period to develop comprehensive theories of the nervous system, looking in particular at the reflex theories of Thomas Laycock (1812–1876) and Charles Sherrington (1857-1952), and the neuron doctrine of Ramon y Cajal (1852-1934).

Pitt Interdisciplinary Biomed Grad Program

INTBP 2100 Biology of Vision CR HRS: 3.0

• Course Coordinators: Rob Shanks, 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 Mathematics

MATH 3375 Computational Neuroscience Methods CR HRS: 3.0 [CNBC core course]
Cross-listed as CMU 36-759

• Instructor: Brent Doiron & Rob Kass
• Location: MI 130
• Days/Times: W/F 1:30PM – 2:50PM

This introductory course in computational neuroscience is intended for a broad range of CNBC students, with backgrounds that may be either technical (math, engineering, statistics, etc.) or non-technical (biology, neuroscience, etc.). The course is co-taught by Brent Doiron and Rob Kass. Pitt students should register in MATH 3375; CMU students may register in 36-759. The two instructors settled on “statistical models” as a unifying theme for the many kinds of models discussed, ranging from those that describe the physiology of neurons to those that describe human behavior. Statistical ideas have been part of neurophysiology since the first probabilistic descriptions of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago; they have been part of experimental psychology even longer. In broad stroke, this course will examine a few of the most important methods and claims that have come from applying statistical thinking to the brain. However, some of the topics involve tools typically taught in statistics courses, while other topics involve tools taught in math courses. Even at an intuitive level, a single course can not provide a comprehensive view of computational neuroscience; the field is too broad. Instead, by studying a series of examples, many of them very influential, students will come away with a sense of the way that computational methods contribute to contemporary understanding of neuroscience.

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: TBA
• Location: Victoria Building 125
• 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: Scott Fraundorf
• 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 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.

PSY 2650 Human Cardiovascular Psychophysiology: CR HRS: 3.0

• Instructor: Peter Gianaros
• Location: TBA
• Days/Times: W 1:00PM – 4:00PM

This course is designed to provide students with (i) a working knowledge of the anatomy and physiology of the cardiovascular system and (ii) a basic understanding of how behavioral factors relate to cardiovascular function, dysfunction, and disease. To these ends, the course is divided into three blocks: lectures, labs, and literatures. In the lectures block, we will spend the first part of the semester covering the fundamentals of cardiovascular, respiratory, and autonomic anatomy and physiology. We will also relate this knowledge to two major forms of cardiovascular pathophysiology that are associated with behavioral factors: hypertension and atherosclerosis. In the labs block, we will spend the second part of the semester collecting, analyzing, and interpreting cardiovascular signals that can be measured non-invasively using psychophysiological methodologies. In the literatures block, we will spend the final part of the semester discussing and critiquing empirical and theoretical papers that span topics of interest in cardiovascular psychophysiology. At the end of these three blocks, students will use what they have learned to write a substantive research proposal on a topic of their choice. The proposal must incorporate the collection (and strong justification and valid interpretation) of at least three cardiovascular, respiratory, or autonomic measures covered in the labs block. It is fine to propose additional, non-cardiovascular measures, but at least three measures from the above domains must be included. Auditing is welcome, but students who audit must register through the university. We are not able to take on students who wish to sit in on an informal basis.