Class Schedule – Fall 2012

Fall 2012

First day of classes: Monday, August 27, 2012

CNBC Core courses:

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

Computational Neuroscience

CMU Biological Sciences

03-763 Advanced Systems Neuroscience: 12 Units [CNBC core course]

• Instructor: Nathan Urban
• Location: Scaife 125
• Days/Times: T/R 9:00AM to 10:20AM (Additional Lecture for Grad Students Thurs 4:30-5:50PM in MI 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.

Prerequisites: 03121 AND (03362 or 03762)

03-815 Magnetic Resonance Imaging in Neuroscience: 12 Units

• Instructor: Eric Ahrens
• Location: Doherty Hall 1209
• Days/Times: T/R 10:30AM to 11:50AM

The course is designed to introduce students to the fundamental principles of magnetic resonance imaging (MRI) and its application in neuroscience. MRI is emerging as the preeminent method to obtain structural and functional information about the living human brain. This methodology has helped to revolutionize neuroscience and the study of human cognition. The specific topics covered in this course will include: introduction to spin gymnastics, survey of imaging methods, structural brain mapping, functional MRI (fMRI), and MR spectroscopy (MRS). Approximately, one third of the course will be devoted to introductory concepts of magnetic resonance, another third to the discussion of MRI methods, and the remaining third will cover a broad range of neuroscience applications. Guest lectures will be incorporated into the course from neuroscientists and psychologists who use MRI in their own research.

CMU CNBC

86-595 Neural Data Analysis: 9 units

• Instructor: Steve Chase
• Date/Time: Tues & Thurs 12:00 PM – 1:20 PM
• Location: MI 355

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 may include neural spike train statistics (Poisson processes, interspike intervals, Fano factor analysis), estimation (MLE, MAP), signal detection theory (d-prime, ROC analysis, psychometric curve fitting), information theory, discrete classification, continuous decoding (PVA, OLE), 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 undergrads 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. Those looking for broader neuroscience application (eg, fMRI) or more focus on regression analysis are encouraged to take 36-746. Those looking for more advanced techniques are encouraged to take 18-699. Prerequisites: undergraduate probability (36-225/227, or its equivalent), some familiarity with linear algebra and Matlab programming

86-712 Computational Neuroscience of Vision : 12 units

• Instructor: Tai Sing Lee
• Date/Time: Mon & Wed 3:00 PM – 4:20 PM
• Location: MI 115

This is a graduate seminar course to study the current literature in computational neuroscience of vision. The course will focus on the interplay between computational approaches and experimental approaches in the study of the visual systems. Instructor will provide context and background on each topic, and guide students’ investigation and presentation of papers. The major theme is to connect statistical scene studies to neural circuits, cortical representation and perception and computation. Topics include, but are not limited to, sensory coding, perceptual organization, hierarchical representation, scene statistics and circuits, predictive models and remapping, attention and saliency, Bayesian inference in perception. Students will be divided into two teams to debate controversial topics.

CMU Computer Science

15-781 Machine Learning: 12 Units

• Instructor: Aarti Singh & Eric Xing
• Location: Wean Hall 7500
• Days/Times: M/W 10:30AM to 11:50AM

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, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. A detailed curriculum from an earlier semester is available at http://www.cs.cmu.edu/%7Etom/10701_sp11/lectures.shtml

CMU Machine Learning

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

• Instructor: Aarti Singh & Eric Xing
• Location: Wean Hall 7500
• Days/Times: M/W 10:30AM to 11:50AM

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, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. A detailed curriculum from an earlier semester is available at http://www.cs.cmu.edu/%7Etom/10701_sp11/lectures.shtml

CMU Psychology

85-712 Cognitve Modeling: 12 units

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. Prerequisites: 85-213

85-714 Cognitve Neuropsychology: 9 units

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?

85-715 Graduate Research Methods: 9 units

The purpose of this course is to enable students to develop a solid understanding of major methodological issues in the study of psychology. The focus will be on issues and techniques that are especially applicable to cognitive, developmental, social, and neuroscience areas, though many of the issues apply to all areas within the field.

85-723 Cognitve Development: 12 units

The general goals of this course are that students become familiar with the basic phenomena and the leading theories of cognitive development, and that they learn to critically evaluate research in the area. Piagetian and information processing approaches will be discussed and contrasted. The focus will be upon the development of childrens information processing capacity and the effect that differences in capacities have upon the childs ability to interact with the environment in problem solving and learning situations.

85-754 Infant Language Development: 12 units

While adults struggle to learn languages, almost all infants acquire language with seemingly little effort. This course examines infants’ learning abilities and language milestones with a focus on several different theoretical accounts of language development, and the way empirical data can be used to assess those theories. The course is reading intensive, and evaluation will be based on both written assignments and oral participation.

85-765 Cognitive Neuroscience: 9 units [CNBC core course]
Cross-listed as Pitt Neuroscience NROSCI 2005.

• Instructor: Carl Olson
• Location: MI 130
• Days/Times: TR 10:30AM to 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.

85-803 Computational Models of Normal and Disordered Cognition: 9 units

CMU Robotics

16-720 Computer Vision: 12 units

• Instructor: Martial Herbert
• Location: Scaife Hall 125
• Days/Times: MW 12:00PM – 1:20PM

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

16-831 Statistical Techniques in Robotics: 12 units

• Instructor: James Bagnell
• Location: Newell Simon Hall 3002
• Days/Times: MW 4:30PM – 5:50PM

Probabilistic and learning techniques are now an essential part of building robots (or embedded systems) designed to operate in the real world. These systems must deal with uncertainty and adapt to changes in the environment by learning from experience. Uncertainty arises from many sources: the inherent limitations in our ability to model the world, noise and perceptual limitations in sensor measurements, and the approximate nature of algorithmic solutions. Building intelligent machines also requires that they adapt to their environment. Few things are more frustrating than machines that repeat the same mistake over and over again. We’ll explore modern learning techniques that are effective at learning online: i.e. throughout the robots operation. We’ll explore how the twin ideas of uncertainty and adaptation are closely tied in both theory and implementation.

CMU Statistics

36-707 Regression Analysis: 12 units

• Instructor: TBA
• Location: Porter Hall 226A
• Days/Times: TR 12:00PM – 1:20PM

This is a course in data analysis using mutiple 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

Cross-listed as 36-309

• Instructor: TBA
• Location: Lecture – Doherty Hall 2315, Sections A, B, C, D – Baker Hall 140 C&F
• Days/Times: Lecture T 12:00PM to 1:20PM. Section A: R 12:00PM to 1:20PM, Section B: R 1:30PM to 2:50PM, Section C: F 12:00PM to 1:20PM and Section D: F 1:30 to 2:50 PM

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 Epidemiology

EPIDEM 2012 Principles of Neuroepidemiology CR HRS: 2.0

• Instructor: Caterina Rosano
• Location: TBA
• Days/Times: R 5:00PM – 6:50PM

The Course in Neuroepidemiology focuses on the application of the methods of epidemiology to the problems of clinical neurology. This course covers epidemiological approaches, etiological perspectives and methodologies to assess disorders of the central nervous system (CNS), including cutting-edge neuroimaging methods. This course also provides guided and critical knowledge of existing neuroepidemiological studies through the research practicum. In addition to students pursuing Doctoral and Master level degrees in Epidemiology, this course is designed to reach trainees in a variety of fields, including medicine, neurology, psychiatry, physical medicine and rehabilitation, neuroscience, psychology and computer science.

Pitt History & Philosophy of Science

HPS 2635 Central Problems in Systems Neuroscience CR HRS: 3.0

• Instructor: Mazviita Chirimuuta
• Location: Cathedral of Learning G28
• Days/Times: W 3:00PM – 5:30PM

This seminar will examine the theoretical foundations of systems and behavioural neuroscience, asking what progress has been made towards a general account of neural processing, and discussing obstacles to theoretical unification. Example seminar topics are: The Neuron Doctrine, Modularity, Understanding Intrinsic Activity, Plasticity. In each case, these issues will be discussed with reference to debates within the philosophy of science. We will ask, for example, whether the neuroscientist encounters particular challenges not otherwise seen in the study of complex systems, and whether the understanding of complexity, emergence and causality elsewhere in biology can be applied to the brain.

Pitt Mathematics

MATH 3375 Computational Neuroscience CR HRS: 3.0 [CNBC core course]

• Instructor: Brent Doiron
• Location: Thackeray 525
• Days/Times: MW 9:30AM-10:45AM

This course will present the fundamentals of neural modeling, with a focus on establishing the computations performed by single neurons and networks of neurons. The aim of the course is to provide students with the necessary knowledge and toolbox from which to simulate neural dynamics within the context of a processing task. Topics to be covered include Hodgkin-Huxley model of a neuron, dendritic integration, reduced neuron models, modeling synaptic dynamics, behavior of small networks of neurons, Weiner analysis of a spike train, spike train statistics, information theory applied to neural ensembles.

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 115
• Days/Times: TR 10:30AM to 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 2041 Developmental Neuroscience: CR HRS: 3.0

• Instructor: TBA
• Location: Langley Hall A221
• Days/Times: TR 4:00PM-5:15PM

This course is designed to provide an overview of principles that govern the developmental assembly of a complex nervous system. Topics covered include formation of neural tube and neural crest, birth and proliferation of neurons, cell migration, neuronal differentiation, synapse formation, synaptic plasticity, development of CNS circuits, and behavior. These topics will be discussed in the context of experimental results obtained by anatomical, biochemical and electrophysiological techniques using vertebrate and invertebrate animals.

NROSCI/MSNBIO 2100 Cellular and Molecular Neurobiology 1: CR HRS: 4.0 [CNBC core course]
NROSCI/MSNBIO 2101 Cellular and Molecular Neurobiology 2: CR HRS: 4.0[CNBC core course]

• Instructor: Carl Lagenaur
• Location: Victoria Building 117 (MTR), Victoria Building 230 (F)
• Days/Time: MTRF 9:00AM to 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 nerve cell biology, protein chemistry, regulation of gene expression, receptor function, and second messenger signaling in a lecture format. A conference designed to develop critical reading skills will cover primary literature corresponding to material covered in each block. Students will be expected to read and discuss original scientific literature.

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, synaptic transmission and neural development.

Prerequisites: A background in basic biology and permission of the instructor is 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 substantial 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: Thomas Olino
• Location: Sennott Square 4125
• Days/Times: M 2:00PM to 4:25PM

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.