Fall 2010

 

First day of classes: CMU Monday, August 23, 2010; Pitt Monday, August 30, 2010

 

CNBC Core courses:

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

Computational Neuroscience, Introduction to Parallel Distributed Processing

 

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 355)

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: Aarti Singh
  • 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.



CMU Electrical & Computer Engineering / Biomedical Engineering

 

18-699 / 42-590   Neural Signal Processing: 12 units

  • Instructor: Byron Yu
  • Location: Scaife Hall 220
  • Days/Times: TR 10:30AM to 11:50AM

The brain is among the most complex systems ever studied. Underlying the brain’s ability to process sensory information and drive motor actions is a network of roughly 1011 neurons, each making 103 connections with other neurons. Modern statistical and machine learning tools are needed to interpret the plethora of neural data being collected, both for (1) furthering our understanding of how the brain works, and (2) designing biomedical devices that interface with the brain.

 

This course will cover a range of statistical methods and their application to neural data analysis. The statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, and Bayesian inference. The neuroscience applications include neural decoding, firing rate estimation, neural system characterization, sensorimotor control, and spike sorting.

 


 

CMU Machine Learning

 

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

  • Instructor: Aarti Singh
  • 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.

 


 

CMU Psychology

 

85-719 Introduction to Parallel Distributed Processing: 9 units  [CNBC core course]

  • Instructor: Dave Plaut
  • Location: Baker Hall 340A
  • Days/Times: TR 10:30AM to 11:50AM

This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representa-tion, and learning. The course will consist of lectures describing the theory behind the models as well as their implementation, and students will get hands-on experience running existing simulation models on workstations.

 

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?

 
85-765 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.

 

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

  • Instructor: Lynne Reder
  • Location: Baker Hall 340A
  • Days/Times: TR 12:00PM to 1:20PM

This is a course on comparison of cognitive architectures. We will discuss a variety of approaches to modeling cognitive phenomena and discuss how each computational model is evaluated. Participation from many graduate students, postdocs and faculty is encouraged. Some weeks we may discuss papers. In addition to papers describing or critiquing architectures (suggestions for specific papers will be sought but I can also propose some), we will also have people in our community present some of their own modeling work and attempt to draw comparisons among approaches to similar problems. The first paper we will discuss (even though faculty were present two years ago when we discussed the pre-print version, this topic is important) is: Roberts, S. and Pashler, H. How persuasive is a good fit? A comment on theory testing. Psychological Review Vol 107(2), Apr 2000, 358-367.

 

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 4215
  • Days/Times: MW 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

 

16-831 Statistical Techniques in Robotics: 12 units

  • Instructor: James Bagnell
  • Location: Gates and Hillman Centers 5222 
  • 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.

 

16-899B Biomechanics and Motor Control: 12 units

  • Instructor: Hartmut Geyer
  • Location: Newell Simon Hall 3002
  • Days/Times: TR 1:30PM – 2:50PM

This course provides an introduction into the mechanics and control of animals and humans.  Because nature has already invented the solutions to many engineering problems, robot designs are often inspired by how humans and other animals move.  Still, there is a large gap between robot and animal performance–imagine a cheetah running, a bird flying, or a dolphin swimming–and many solutions are yet to be discovered.

 

The course introduces the basic components common to these animal machines and how they work from an engineering perspective.  The main topics covered include muscle-skeleton mechanics and neural control applied to flying, swimming, and legged locomotion.  With an emphasis on the latter application, examples of bio-inspiration in robots and rehabilitation devices are highlighted.

 

By the end of the course, you will have the basic knowledge to build your own dynamic models of animal and human motions.  The course is structured in two parts.  The first part will have two 2h weekly lectures and accompanying assignments.  In the second part, team projects will let you apply your knowledge to problems of animal and human motion in theory and computer simulations.

 


 

CMU Statistics

 

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 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: Brent Doiron
  • Location: Allen Hall 105
  • Days/Times: MW 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: Stephen Meriney
  • 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 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

  • 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 2465 Perception and Attention: CR HRS: 3.0

  • Instructor: Walt Schneider
  • Location: Learning Research & Development Center Room TBA
  • Days/Times: T 3:00pm – 5:50pm

 

PSY 2575 Topics Seminar in Health Psychology:  Neuroimaging: CR HRS: 2.0-3.0

  • Instructor: Kirk Erickson
  • Location:Sennott Square 4117
  • Days/Times: M 3:00pm – 5:50pm

This seminar will focus on both fundamental and current research employing structural and functional MRI methods.  Early research on the nature of the functional MRI response and neurovascular coupling will be discussed as well as designing experiments and techniques for analyzing data.  In the last half of the semester, current research that either extends MRI methodology or relates the method to a new question will be discussed.  The goal of the class is for students to learn about the evolution and development of neuroimaging techniques, to understand the strengths and limitations of neuroimaging methods, and to become more comfortable with reading, interpreting, and critiquing neuroimaging manuscripts.  Students with an interest in understanding neuroimaging methods, but without much background, are encouraged to attend or email the instructor for more information.

 

Senior level undergraduates and graduate students interested in the course are encouraged to email the instructor for further information.