Fall 2008

First day of classes for CMU & Pitt: Monday, August 25, 2008.

CNBC Core courses:

Advanced Cellular Neuroscience, Cognitive Neuroscience,
Cellular & Molecular Neurobiology, Computational Neuroscience,
Functional Neuroanatomy

CMU Biological Sciences

03-762 Advanced Cellular Neuroscience: 9 Units [CNBC core course]

• Instructor: N. Urban
• Location: Doherty Hall 2210
• Days/Times: T/R 9:00AM to 10:20AM

This course is a graduate version of 03-362. Students will attend the same lectures as the students in 03-362, plus an additional once weekly meeting. In this meeting topics covered in the lectures are 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 including work by Hodgkin and Huxley on action potentials and by Katz and Eccles on synaptic transmission. Generation and use of genetically modified animals also will be discussed. Performance in this portion of the class will be assessed by supplemental exam questions.

Prerequisites: 03121

CMU Biomedical Engineering

42-731 Introduction to Biomedical Imaging and Image Analysis: 12 Units

• Instructor: TBA
• Location: Porter Hall A19
• Days/Times: T/R 2:30PM to 4:20PM

The goals of this course are to provide students with the following: the ability to use mathematical techniques such as linear algebra. Fourier theory and sampling in more advanced signal processing settings; fundamentals of multiresolution and wavelet techniques; and in-depth coverage of some bioimaging applications such as compression and denoising. Upon successful completion of this course, the student will be able to: explain the importance and use of signal representations in building more sophisticated signal processing tools, such as wavelets; think in basic time-frequency terms; describe how Fourier theory fits in a bigger picture of signal representations; use basic multirate building blocks, such as a two-channel filter bank; characterize the discrete wavelet transform and its variations; construct a time-frequency decomposition to fit a given signal; explain how these tools are used in various applications; and apply these concepts to solve a practical bioimaging problem through an independent project. Pre-requisite: 18-791, or permission of instructor. (Also known as 18-799)

CMU Computer Science — Classes will not begin until September 8, 2008 — after the CSD Ph.D. Immigration Course

15-781 Artificial Intelligence: Machine Learning: 12 Units

• Instructor: Poe Xing
• Location: Wean Hall 5409
• 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 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.

15-871 Computational Methods for Biological Modeling and Simulation: 12 units

• Instructor: Russell Schwartz
• Location: TBD
• Days/Times: T/R 10:30AM to 11:15AM

This course is designed to teach computational aspects of using modeling and simulation methods to understand biological systems, with an emphasis on practical application. The course will be divided into three general topics: models for optimization problems, simulation and sampling, and model parameter tuning. Specific model types to be covered will include graph models in evolution, string models for biological sequence data, Markov chain Monte Carlo models, hidden Markov models, and discrete-event models, as well as examples of special-purpose models important to specific sub-disciplines of biology. Algorithmic techniques to be studied will include common algorithms for graph optimization problems, mathematical programming methods, event queue data structures, key machine learning methods, commonly used heuristic methods, and methods for generating random numbers and accurately sampling from probability distributions required by or implicit in mathematical models. All of the above will be illustrated with examples from molecular, cellular, or evolutionary biology.

Registration in this course is restricted to SCS PhD students. Others wishing to enroll should contact the instructor, obtain written (email) permission and forward the email to This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

CMU Machine Learning

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

• Instructors: Poe Xing
• Location: Wean Hall 5409
• Days/Times: T/R 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 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.

This course is only available to CSD PhD and 5th year MS students. If you are from another department you must register under the number 10-701 (MLD). If you have questions send email to diane@cs

CMU Psychology

The themes of the course are: What is the architecture of cognition, and how is it neurally instantiated? The pedagogical goals are to impart basic knowledge of cognitive science and cognitive neuroscience, while facilitating the transition from basic material in secondary texts to thoughtful analysis and integration of the primary research literature. The course will be divided into five units and a wrap-up session. There will be an evaluation after each unit following the first. GRADUATE STUDENTS ONLY.

Languages may be the most complex systems people ever master, and yet infants appear to learn them effortlessly. By contrast, adults often struggle to acquire language. This class will explore theoretical controversies and experimental results in an attempt to understand how infants acquire language, and the way that acquisition might differ between infancy and adulthood. Throughout the course, there will be a focus on the potential role of learning in language acquisition, the strengths and limitations of the experimental methods that are appropriate for use with infants, and the relation between theoretical constructs and experimental results. The course will be reading intensive, and evaluation will be based upon both written assignments and oral participation.

Special Permission Required: Please email Dr. Erik Thiessen at This e-mail address is being protected from spambots. You need JavaScript enabled to view it for instructors permission for graduate course. Erin Donahoe in BH 342E can register you.

• Instructor: Carl Olson : 9 units

• 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 vison, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, for example, computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans, and single-neuron recording in animals. Lectures will alternate with sessions in seminar format. Prerequisites: Graduate standing or two upper-level psychology courses from the areas of developmental psychology, cognitive psychology, computational modeling of intelligence, neuropsychology or neuroscience.
  
  Special permission is required: Graduate Students, instructors permission from Carl Olson at This e-mail address is being protected from spambots. You need JavaScript enabled to view it and once you have instructor's permission, please see Erin Donahoe , in BH 342 E or This e-mail address is being protected from spambots. You need JavaScript enabled to view it 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.

85-790 Human Memory: 9 units

CMU Robotics

16-720 Computer Vision: 12 units

• Instructor: Martial Herbert
• Location: Porter Hall A18B
• Days/Times: TR 1:30PM - 2:50PM

This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision. Topics covered include image formation and representation, camera geometry and calibration, multi-scale analysis, segmentation, contour and region analysis, energy-based techniques, reconstruction of based on stereo, shading and motion, 3-D surface representation and projection, and analysis and recognition of objects and scenes using statistical and model-based techniques. The material is based on a recent graduate-level textbook augmented with research papers, as appropriate. The course involves considerable Matlab programming exercises.

The textbook is recommended, and not required.

Textbook Information:
Title: "Computer Vision: A Modern Approach"
Authors: David Forsyth and Jean Ponce
Publisher: Prentice Hall
ISBN: 0-13-085198-1

16-823 Physics-based Methods in Vision (Appearance Modeling): 12 units

• Instructor: Srinivasa Narasimhan
• Location: Wean Hall 4615A
• Days/Times: M/W 1:30PM - 2:50PM

Everyday we observe an extraordinary array of light and color phenomena around us, ranging from the dazzling effects of the atmosphere, the complex appearances of surfaces and materials and underwater scenarios. For a long time, artists, scientists and photographers have been fascinated by these effects, and have focused their attention on capturing and understanding these phenomena. In this course, we take a computational approach to modeling and analyzing these phenomena, which we collectively call as "visual appearance". The first half of the course focuses on the physical fundamentals of visual appearance, while the second half of the course focuses on algorithms and applications in a variety of fields such as computer vision, graphics and remote sensing and technologies such as underwater and aerial imaging.

This course is an initial attempt to unify concepts usually learnt in physical sciences and their application in imaging sciences. The course will also include a photography competition in addition to analytical and practical assignments.

Prerequisites: The prerequisite will be an undergraduate or graduate class in Computer Vision or in Computer Graphics. 15385 or 15462 or 16720

Text: There will be no required text but some course notes and papers will be distributed. The assignments and projects will be mostly in Matlab.

16-831 Statistical Techniques in Robotics: 12 units

• Instructor: James Bagnell
• Location: Newell Simon 1305
• Days/Times: T/R 12:00PM - 1:20PM

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

36-729 & 36-730 Time Series and Point Processes, I and II: 6 units each

• Instuctor: Rob Kass

• Tentative time/location: Monday and Wednesday, 10:30-11:50, Hamburg Hall 1004. Both may change if there are conflicts among many interested students. Anyone interested in this course should contact Rob Kass immediately at This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

   

  This course provides a foundation for statistical analysis of continous time-varying signals (time series) and sequences of event times (point processes).  Examples will involve data from neural spike trains, EEG, EMG, MEG, and fMRI.  Topics are expected to include the following: preliminaries on trigonometric functions and series; harmonic analysis and the periodogram; smoothing and spectral estimation; ARMA models; Poisson and non-Poisson point processes; generalized regression modeling; and bivariate time series, including coherence and Granger causality.

   

  Please note that the semester is broken into two mini-semester courses to accommodate the possibility of taking only the first half of the course, which will emphasize time series. The course is intended for both statistics graduate students and neuroscience or bioengineering students; the prerequisite is knowledge of maximum likelihood and generalized regression at the level of Statistical Methods in Neuroscience and Psychology (36-746).

36-749 Experimental Design for Behavioral and Social Sciences: 12 units
Cross-listed as 36-309

• Instructor: Howard Seltman
• Location: Lecture - Porter Hall 100, Sections A, B, C, D - Baker Hall 140 C&F
• Days/Times: Lecture TR.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 Bioengineering 

BIOE 2600 Neuroimaging CR HRS: 3.0
(Cross listed as CMU 42-709)

• Instructor: Dr. Pravat Mandal
  Co-Instructor: Dr. Jelena Kovacevic
• Location: Benedum Hall 720
• Days/Times: Tue/Fri 9:00AM to 10:25AM

This course consists of six state-of-the-art imaging techniques (i.e., MRI, MRS, fMRI, PET, MEG/EEG and Optical). Each part of the module will present indepth analysis of the each technique and its application in neuroscience research. Apart from in-depth presentation of the each technique, students will also get acquainted with the operation of the respective instruments. Tour to that respective facility will be guided by the concerned faculty and scientific staff member in that respective facility will assist for demonstration. This course is a joint program between PITT and CMU.

References:

• The Essential Physics of Medical Imaging (J. T. Bushberg et al.)

• Magnetic Resonance Spectroscopy and its application in Alzheimer's disease. Concepts in Magnetic Resonance, Pravat K Mandal, Vol 30A 1-25 (2007)

• MRI in practice (Westbrook, Roth and Talbot)

Pitt Mathematics

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

• Instructor: Brent Doiron
• Location: 106 Alexander J. Allen Hall
• Days/Times: MWF 1:00PM - 1:50PM

  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 : 9 units
• 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: Graduate standing or permission of the instructor.

 

NROSCI 2011 Functional Neuroanatomy: CR HRS: 4.0 [CNBC core course]

• Instructor: Susan Sesack
• Location:  Clapp 000L9
• Days/Times: M/F 10:00 AM to 10:50 AM; W 10:00 AM to 11:50 AM

This course covers the basic structure of the central nervous system from spinal cord to cerebral cortex. The major sensory, motor and integrative neural systems of the human brain are discussed. Based on an understanding of normal neural connections and brain function, the anatomical and physiological basis of various neurological disorders of the nervous system will be explored.

Prerequisites: NROSCI 1000 or 1003. Special Enrollment Counseling is required for registration. Students should contact Dr. Sesack for permission to register.

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: CL 00139 (MTRF)
• 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 of the 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

• Instructors: TBA
• Location: SENSQ 4125
• Days/Times: T 9:30AM to 11:55AM

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 2205 Psychopathology: CR HRS: 3.0

• Instructor: Michael Pogue-Geile
• Location:  SENSQ 4125
• Days/Times: TR 2:30PM to 3:45PM

This graduate course provides a critical background in research strategies, phenomena, empirical research, and models of adult psychopathology. The course emphasis will be on etiological and pathological research, with both psychological and biological findings to be discussed. Course concentration will be on the major psychopathologies with clinical onset in adulthood, including schizophrenia, affective disorders, anxiety, addictions, and eating disorders. Conceptual and methodological issues that cross diagnostic categories will be stressed. Treatment approaches and differential diagnosis will be covered but not emphasized.

Prerequisites: Permission of the instructor if not psychology graduate student.

PSY 2476 Topics in Cognitive Psychology: Functional MRI: CR HRS: 3.0

• Instructor:
• Location:
• Days/Times:

An introduction to MRI methods, design, and analysis related to cognitive and behavioral functional MRI. This course will cover basic principles of MR signal formation and measurement, physiological mechanisms underlying BOLD fMRI signal, pulse sequences, signal and noise, functional imaging techniques, experimental design, and basic statistical analysis. This course is open to graduate students at 3 credits. Advanced undergraduate students may enroll with permission of instructor. Other students may enroll at 2-3 credits.