Fall 2011

 

First day of classes: Monday, August 29, 2011

 

CNBC Core courses:

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

Computational Neuroscience, Computational Models of Neural Systems

 

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

 

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

  • Instructor: Nathan Urban
  • Location: Scaife 125
  • Days/Times: T/R 9:00AM to 10:20AM (Additional Lecture for Grad Students day/time TBA)

This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.

Prerequisites:     03121 AND (03362 or 03762)

 

03-815 Magnetic Resonance Imaging in Neuroscience: 12 Units 

  • Instructor: Eric Ahrens
  • Location: Mellon Institute 348
  • Days/Times: T/R 10:30AM to 11:50AM

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

 


 

CMU CNBC

 

86-671 Philosophy of Perception : 12 units

  • Instructor: Wayne Wu
  • Date/Time: Tues & Thurs 3:00 PM – 4:20 PM
  • Location: MI 130

This course addresses questions concerning the nature of perception. We?ll attempt to answer a variety of questions including (but not limited to): What is perception? How is it different from thought? Must perception be conscious? Can there by unconscious perception? Do we perceive the world directly? Do we see only images? Does perception represent the world? How many senses are there and how do we divide them? How is vision different from touch or audition? Is taste a sense? Is smell? Is proprioception or the sense of pain? What are hallucinations? Is imagination a form of perception? What are colors, sounds as objects of perception? Is there a difference between silence and deafness? Do we perceive only what we attend? How can understanding the brain help us understand perception? At every point, we will keep touch with the empirical literature.


 

CMU Computer Science

 

15-781 Machine Learning: 12 Units

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

Machine learning studies the question “How can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

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

 

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 neural systems from a computer science perspective. We will examine several brain areas, such as the hippocampus and cerebellum, 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 Electrical & Computer Engineering

 

18-699 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.

Prerequisites: 18-290; 36-217, or equivalent introductory probability theory and random variables course; an introductory linear algebra course; senior or graduate standing. No prior knowledge of neuroscience is needed

 


 

CMU Machine Learning

 

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

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

Machine learning studies the question “How can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

 

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

 


 

CMU Psychology

 

85-723 Cognitve Development: 9 units

  • Instructor: Robert Siegler
  • Location: Baker Hall 340A
  • Days/Times: TR 1:30PM to 2:50PM

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

 

85-754 Infant Language Development: 9 units

  • Instructor: Erik Thiessen
  • Location: Porter Hall A19D
  • Days/Times: MW 10:30AM to 11:50AM

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

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

  • Instructor: Carl Olson
  • Location: MI 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@cnbc.cmu.edu and once you have instructor’s permission, please see Erin Donahoe , in BH 342 E or donahoe@andrew.cmu.edu to register you.

 

85-770 Perception: 9 units

  • Instructor: Laurie Heller
  • Location: Baker Hall 336B
  • Days/Times: TR 12:00PM to 1:20PM

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 336B
  • Days/Times: MW 3:00PM to 4:20PM

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 IQ?s 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.

 


 

CMU Robotics

 

16-720 Computer Vision: 12 units

  • Instructor: Martial Herbert
  • Location: Gates and Hillman Centers 4307
  • Days/Times: MW 1:30PM – 2:50PM

This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and final project. The homeworks involve considerable Matlab programming exercises.

Texts recommended, and not required.

Title: “Computer Vision Algorithms and Applications”
Series: Texts in Computer Science
Publisher: Springer
ISBN: 978-1-84882-934-3

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

 

16-831 Statistical Techniques in Robotics: 12 units

  • Instructor: James Bagnell
  • Location: Gates and Hillman Centers 5222 
  • Days/Times: TR 4:30PM – 5:50PM

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

 

16-899B Biomechanics and Motor Control: 12 units

  • Instructor: Hartmut Geyer
  • Location: Newell Simon Hall 1305
  • Days/Times: TR 1:30PM – 2:50PM

TThis course provides an introduction into the mechanics and control of animals and humans. Because nature has already invented the solutions to many engineering problems, today’s robot designs are often inspired by how humans and other animals move. Still, there is a large gap between robot and animal performance. 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.

 

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

Statistical aspects of the design and analysis of planned experiments are studied in this course. A clear statement of the experimental factors will be emphasized. The design aspect will concentrate on choice of models, sample size and order of experimentation. The analysis phase will cover data collection and computation, especially analysis of variance, and will stress the interpretation of results. In addition to weekly lecture, students will attend a computer lab once a week. Prerequisite: 36-202, 36-220, or 36-247

 


 

Pitt Mathematics

 

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

  • Instructor: Bard Ermentrout
  • Location: Thackeray 525
  • 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: Permission of the instructor.

    NROSCI 2041 Developmental Neuroscience: CR HRS: 3.0

    • Instructor: 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

    • Instructor: 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 2410 Foundations of Cognitive Psychology: CR HRS: 3.0

    • Instructor: Christian Schunn
    • Location: Learning Research & Development Center Room TBA
    • Days/Times: W 2:30pm – 5:20pm

    This course will introduce core issues, theories, and experimental findings in cognitive psychology. Topics to be covered include history of cognitive psychology, sensory perception, attention, memory, imagery, language, reasoning, learning and expertise, problem solving, decision making, and individual differences in cognition. The class format will be a small amount of lecture and a large amount of in-class discussion. You will be expected to understand these foundational theories and issues as well as the research methods used this area—in other words, how human cognition can be studied scientifically, and why the results of experimental investigations support particular theories. You will also learn something about how to write and present effective arguments on these topics to your peers—to argue points in a way that makes your arguments clearly presented, logically structured, and reflects insightful and reflective thinking. Most importantly, it is my hope that you will find deep connections between aspects of this course and your current and future professional life.