Fall 2008

 

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

CNBC Core courses:

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

Note:  students in
the CNBC graduate training program automatically have
permission to attend the core courses listed above.

 


 

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 ScienceClasses 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 deb@cs.cmu.edu.

 


 

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

 

85-706 Graduate Core Course Cognitive Psychology: 9 units

  • Instructor: TBA
  • Location: Baker Hall 340J
  • Days/Times: W 1:30PM to 4:20PM

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
.

 

85-754 Language Acquisition in Infancy and Childhood: 9 units

  • Instructor: Erik Thiessen
  • Location: Baker Hall 336B
  • Days/Times: MW 1:30PM to 2:50PM

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
thiesen@andrew.cmu.edu for instructors permission for graduate course.
Erin Donahoe in BH 342E can register you.

   

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

  • 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 colson@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: 9 units

  • Instructor: Roberta Klatzky
  • Location: Baker Hall 336B
  • Days/Times: TR 9:00AM to 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: 9 units

  • Instructor: Lynne Reder
  • Location: Baker Hall 340A
  • Days/Times: M/W 1:30PM to 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.

 

85-806 Autism: Psychological and Neuroscience Perspectives: 9 units

  • Instructor: Marcel Just
  • Location: Baker Hall 336B
  • Days/Times: W 7:00PM to 9:50PM

    Autism is a disorder that affects many cognitive and social processes,
    sparing some facets of thought while strongly impacting others. This
    seminar will examine the scientific research that has illuminated the
    nature of autism, focusing on its cognitive and biological aspects. For
    example, language, perception, and theory of mind are affected in
    autism. The readings will include a few short books and many primary
    journal articles. The readings will deal primarily with autism in
    people whose IQs are in the normal range (high functioning autism).
    Seminar members will be expected to regularly enter to class
    discussions and make presentations based on the readings.
     
    The seminar will examine various domains of thinking and various
    biological underpinnings of brain function, to converge on the most
    recent scientific consensus on the biological and psychological
    characterization of autism.  There will be a special focus on brain
    imaging studies of autism, including both structural (MRI) imaging of
    brain morphology and functional (fMRI and PET) imaging of brain
    activation during the performance of various tasks.
     
    Prerequisites: 85:211 or 85:213 or 85:219 or 85:355 or 85:429

 


 

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 kass@stat.cmu.edu.

     

    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.