Fall 2009

 

First day of classes: CMU Monday, August 24, 2009; Pitt Monday, August 31, 2009

 

CNBC Core courses:

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

Computational Models of Neural Systems, Computational Neuroscience

 

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

 


 

CMU Biological Sciences

 

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

  • Instructor: N. Urban
  • Location: Doherty Hall 2210
  • Days/Times: T/R 9:00AM to 10:20AM (Additional Lecture for Grad Students Fridays 10:00AM to 11:20AM in MI 191)

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 Eccleson 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: 03-121

 


 

CMU Computer Science

 

15-781 Artificial Intelligence: Machine Learning: 12 Units

  • Instructor: Carlos Guestrin
  • Location: Wean Hall 7500
  • Days/Times: M/W 10:30AM to 11:50AM

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics.

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.

 

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

  • Instructor: Russell Schwartz
  • Location: Porter Hall A21
  • Days/Times: T/R 3:00PM to 4:20PM

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 inmathematical models. All of the above will be illustrated with examples from molecular, cellular, or evolutionary biology.

 

Registrationin 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.

 

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

  • Instructor: Dave Touretzky
  • Location: Gates and Hillman Centers 4101
  • Days/Times: M/W 4:30PM to 5:50PM

This course is an in-depth study of information processing in real neuralsystems from a computer science perspective. We will examine several brain areas 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 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)

  • Instructor: Carlos Guestrin
  • Location: Wean Hall 7500
  • Days/Times: M/W 10:30AM to 11:50AM

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics.

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.

 


 

CMU Psychology

 

85-721 Language and Thought: 9 units

  • Instructor: Brian MacWhinney
  • Location: Baker Hall 340A
  • Days/Times: MW 10:30AM to 11:50AM

This course allows the student to explore ways in which the mind shapeslanguage and language shapes the mind. Why are humans the only specieswith a full linguistic system? Some of the questions to be exploredare: What kinds of mental abilities allow the child to learn language?What are the cognitive abilities needed to support the production andcomprehension of sentences in real time? How do these abilities differbetween people? Are there universal limits on the ways in whichlanguages differ? Where do these limitations come from cognition ingeneral or the specific language facility? Why is it so hard to learn asecond language? Are there important links between language change andcultural change that point to links between language and culture?

 

85-721 Music and Mind: The Cognitive Neuroscience of Sound: 9 units

  • Instructor: Lori Holt
  • Location: Baker Hall 336B
  • Days/Times: MW 9:00AM to 10:20AM

This course will take a multidisciplinary approach to understand the neural systems that contribute to auditory perception and cognition, using music and speech as domains of inquiry. Students will master topics in acoustics, psychophysics, cognitive psychology, cognitive development, neurophysiology, and neuropsychology. The early part of the course will provide students with a common foundation in acoustics, signal processing, and auditory neuroscience. Later in the semester, the focus will turn to developing analytical skills through critical evaluation of primary-source experimental literature. Hands-on laboratories and homework sets in sound manipulation and experimentation also will constitute a means of learning about auditory cognitive neuroscience. Throughout, the focus will be upon understanding general cognitive and perceptual challenges in perceiving and producing complex sounds like speech and music. Topics may include biological vs. cultural influences, development in infancy, perception versus production, time perception, effects of experience on perceptual processing, comparative studies of animals, attention, development of expertise, effects of brain damage, and emotional expression. Topics will be addressed from the perspective of cognitive neuroscience, in that we will attempt to understand the neural processes that give rise to auditory perception and cognition.

 

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

  • Instructor: Carl Olson
  • Location: MI 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.

 

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: MW 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: T 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: Gates and Hillman Centers 4307
  • Days/Times: MW 3:00PM – 4:20PM

This course deals with the science and engineering of computer vision,that is, the analysis of patterns in visual images of the world withthe goal of reconstructing and understanding the objects and processesin the world that are producing them. The emphasis is on physical,mathematical, and information processing aspects of vision. Topicscovered include image formation and representation, camera geometry andcalibration, multi-scale analysis, segmentation, contour and regionanalysis, energy-based techniques, reconstruction of based on stereo,shading and motion, 3-D surface representation and projection, andanalysis and recognition of objects and scenes using statistical andmodel-based techniques. The material is based on a recentgraduate-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

 

16-831 Statistical Techniques in Robotics: 12 units

  • Instructor: James Bagnell
  • Location: Wean Hall 4615A
  • Days/Times: TR 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

 

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

Cross-listed as 36-309

  • Instructor: TBA
  • Location: Lecture – Porter Hall 100, 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 Mathematics

 

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

  • Instructor: Bard Ermentrout
  • Location: Benedum 520
  • Days/Times: MWF 1:00PM – 2:15PM

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: Graduate standing or permission of the instructor.

NROSCI 2041 Developmental Neuroscience: CR HRS: 3.0

  • Instructors: Emily Drill
  • Location: Langley Hall A221
  • Days/Times: TR 2:30PM to 3:45PM

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 116
  • 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 graduatesequence designed to provide an overview of cellular and molecularaspects of neuroscience. This course covers nerve cell biology, proteinchemistry, regulation of gene expression, receptor function, and secondmessenger signaling in a lecture format. A conference designed todevelop critical reading skills will cover primary literaturecorresponding to material covered in each block. Students will beexpected to read and discuss original scientific literature.

2101- This course is the second component of the introductory graduatesequence designed to provide an overview of cellular and molecularaspects of neuroscience. This course covers the electrical propertiesof 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 studentsto enroll in a primary choice (course), and a secondary choice in casethe primary is not available. Please register for the NROSCI sectionsas your primary chioce and the MSNBIO sections as your secondarychoice, so that when NROSCI fills up, the Registrar’s Office willautomatically put you in the MSNBIO section without having to completeany 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 timeto background reading might prefer to take Advanced Cellular Neuroscienceinstead.

 


 

Pitt Psychology

 

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

  • Instructors: Jeewon Cheong
  • 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.

 

PSY 2475 Behavioral Neuroscience: CR HRS: 3.0

  • Instructor: Julie Fiez
  • Location: Learning Research & Development Center Room TBA
  • Days/Times: M 11:30AM to 1:55PM

Methods and data from the fields of neuroscience and cognitive neuroscience are beginning to play an increasingly important role in the development of basic theories about cognitive function, and in our understanding of clinical disorders such as depression and addiction. Many graduate students in psychology are not able to take full advantage of these related areas of study, however, because they lack prior exposure to basic biological and neuroscientific facts and methodologies. The objectives of this course are to: 1) introduce basic facts and methods of systems and cognitive neuroscience, cellular and pharmacological neuroscience, and molecular neuroscience; 2) provide a neuroscientific overview of cognitive topics (e.g., perception, language, emotion), and 3) provide a neuroscientific overview of disordered and impaired cognition (e.g, depression, Schizophrenia) The proposed course will be open to all graduate students, but it is specifically designed to be a required core course in the Clinical and Cognitive graduate training programs in the Department of Psychology.

 

PSY 2476 Topics in Cognitive Psychology: Brain Connection Mapping: CR HRS: 3.0

  • Instructor: Walt Schneider
  • Location: Learning Research & Development Center Room TBA
  • Days/Times: T 2:00PM – 4:45PM, W 12:00PM to 12:55PM

Description TBA