Fall 2015

First day of classes: Monday, August 31, 2015

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: Aryn Gittis
  • Days/Times: T/R 9:00AM – 10:20AM (Doherty Hall 2302), T 3:30PM – 4:50PM (Location TBA)

This course is an introductory graduate course in cellular neuroscience. As such it will assume little or no background but will rapidly progress to discussions of papers from the primarily literature. The structure of the course will be about half lectures and half discussions of new and classic papers from the primary literature. These discussions will be substantially led by students in the course. Topics covered will include ion channels and excitability, synaptic transmission and plasticity, molecular understanding of brain disease and cell biology of neurons. Assessment will be based on class participation, including performance on in-class presentations and a writing assignment.

CMU Biomedical Engineering

42-431 Introduction to Biomedical Imaging and Image Analysis : 12 Units

  • Instructor: Gustavo Rohde
  • Location: Porter Hall 226C
  • Days/Times: T/R 1:30PM – 2:50PM

The aim of this course is to prepare upper level undergraduates so that they can be productive when faced with technical problems related to biomedical imaging. The basic underlying techniques (mathematics, physics, signal processing, data analysis) for understanding the several phenomena related to image formation in biomedical devices are presented. Several methods for computational information extraction from image data are also presented (segmentation, registration, pattern recognition, etc.). Course work will include homework assignments (including analytical and programming exercises) as well as an independent project. Field trips to observe biomedical imaging devices in action are also planned. Prerequisite: 18-396 Signals and Systems (or 18-290) or permission of the instructor, working knowledge of Matlab, and some image processing experience.


86-631 Neural Data Analysis: 9 units
(Cross listed as 42-631)

  • Instructor: Steve Chase
  • Date/Time: T/R 1:30 PM – 2:50 PM
  • Location: MI 130

The vast majority of behaviorally relevant information is transmitted through the brain by neurons as trains of actions potentials. How can we understand the information being transmitted? This class will cover the basic engineering and statistical tools in common use for analyzing neural spike train data, with an emphasis on hands-on application. Topics will include neural spike train statistics, estimation theory (MLE, MAP), signal detection theory (d-prime, ROC analysis), information theory (entropy, mutual information, neural coding theories, spike-distance metrics), discrete classification (naïve Bayes), continuous decoding (PVA, OLE, Kalman), and white-noise analysis. Each topic covered will be linked back to the central ideas from undergraduate probability, and each assignment will involve actual analysis of neural data, either real or simulated, using Matlab. This class is meant for upper-level undergraduates or beginning graduate students, and is geared to the engineer who wants to learn the neurophysiologist’s toolbox and the neurophysiologist who wants to learn new tools. This course leads naturally into 42/18-632, Neural Signal Processing. Prerequisites: undergraduate probability (36-225/227, or its equivalent), some familiarity with linear algebra, and Matlab programming.

86-675 Computational Perception : 12 units

  • Instructor: Tai Sing Lee
  • Date/Time: M/W 1:30PM – 2:50PM
  • Location: Porter Hall 226C

In this course, we will first cover the biological and psychological foundational knowledge of biological perceptual systems, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. The course will focus on vision this year, but will also touch upon other sensory modalities. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. Topics include perceptual representation and inference, perceptual organization, perceptual constancy, object recognition, learning and scene analysis. Prerequisites: First year college calculus, some basic knowledge of linear algebra and probability and some programming experience are desirable.

86-716 The Neuroscience of Consciousness : 6 units

  • Instructor: Wayne Wu
  • Date/Time: W 9:00AM – 12:00PM
  • Location: Mellon Institute 115

This is a survey course critically examining the central neurobiological theories of consciousness, that is, theories that attempt to understand consciousness with an eye towards how the brain produces it. We will discuss what consciousness might be? In addition, we will spend some time discussing what it means to explain psychological phenomena by appeal to neuroscience. We will then examine theoretical work by Block, Damasio, Dehaene, Koch, Lamme, and Tononi among others. The relation of consciousness to attention, working memory, informational integration, predictive coding and other key concepts will be explored. We might explore some case studies to put the theories into action including blindsight, zombie action and other cases. Some time is reserved to explore relevant phenomena of interest to participants, including discussion of current work in the CNBC. This is a graduate course, limited to CNBC graduate students or graduate students from other related disciplines who have a background in neuroscience, psychology, philosophy or related fields. Students should contact instructor if there are any questions.

CMU Computer Science

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

  • Instructor: Dave Touretzky
  • Location: Gates Hillman 4301
  • Days/Times: M/W 4:30PM – 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 Machine Learning

10-701 Introduction to Machine Learning: 12 units

  • Instructors: Ziv Bar-Joseph & Poe Xing
  • Location: Wean Hall 7500
  • Days/Times: T/R 12:00PM – 1:20PM

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

85-708 Visual Cognition: 9 units

  • Instructor: David Plaut
  • Location: Baker Hall 342E
  • Days/Times: T/R 10:30AM – 11:50AM

Recognizing an object, face or word is a complex process which is mastered with little effort by humans. This course adopts a three-pronged approach, drawing on psychological, neural and computational models to explore a range of topics including early vision, visual attention, face recognition, reading, object recognition, and visual imagery. The course will take a seminar format. This course number is for Psychology Graduate Students only. Please email Dr. David Plaut at plaut@cmu.edu for instructors permission and CC the Graduate Coordinator, Erin Donahoe at donahoe@andrew.cmu.edu to register you.

85-765 Cognitive Neuroscience: 12 units [CNBC core course]
Cross-listed as Pitt Neuroscience NROSCI 2005.

  • Instructor: Carl Olson
  • Location: MI 130
  • Days/Times: T/R 10:30AM – 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: 12 units

  • Instructor: Roberta Klatzky
  • Location: Baker Hall 336B
  • Days/Times: T/R 9:00AM – 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: T/R 10:30AM – 11:50AM

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: 12 units

  • Instructor: Marcel Just
  • Location: Baker Hall 336B
  • Days/Times: W 7:00PM – 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: Srinivasa Narasimhan & Abhinav Gupta
  • Location: Doherty Hall 2315
  • Days/Times: M/W 4:30PM – 5:50PM

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

CMU Statistics

36-707 Regression Analysis: 12 units

  • Instructor: Valerie Ventura
  • Location: Porter Hall 226A
  • Days/Times: T/R 12:00PM – 1:20PM

This is a course in data analysis using mutiple linear regression. Topics covered include simple linear regression, ordinary least squares and weighted least squares, the geometry of least squares, quadratic forms, F tests and ANOVA tables, residuals, outlier detection, and identification of influential observations, variable selection methods, and modern regression techniques. Essential background in linear algebra is reviewed where necessary. When time permits other topics such as nonlinear regression and robust estimation will be discussed. Practice in data analysis is obtained through course projects.

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

Cross-listed as 36-309

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

BIOENG 2186 Neural Engineering CR HRS: 3.0

  • Instructor: Aaron Batista & Neeraj Gandhi
  • Location: Benedum Hall G24
  • Days/Times: M/W/F 1:00PM – 1:50PM

Neural Engineering is an emerging discipline that seeks first, to understand brain function using computational and engineering principles; second, to improve health through nervous system interventions; and third, to discover principles of biological information processing that can improve computing technologies. Students will learn the principles of neuroscience and the computational tools needed for original research in neural engineering. They will develop the ability to critically evaluate scientific evidence. They will design novel experiments and approaches in neuroscience and neural engineering.

BIOENG 2650 Learning & Control and Movement CR HRS: 3.0

  • Instructor: Gelsy Torres
  • Location: Benedum Hall 320
  • Days/Times: M/W 3:00PM – 4:15PM

In this course we will discuss current theories on how the human motor system controls and learns movement. These theories are developed blending concepts from neuroscience, probability, and biomechanics. While motor control will be discussed as a feedback control problem, these theories will be compared to what we know about the motor system.
Probability foundations will be used as a framework to model how the nervous system updates estimates of limb position and sensory feedback during movements. We will consider how disease can inform us about principles of movement control and motor learning.

Pitt History and Philosophy of Science

HPS 2635 Central Problems in Systems Neuroscience: How to simplify the brain CR HRS: 3.0

  • Instructor: Mazviita Chirimuuta
  • Location: Cathedral of Learning G28
  • Days/Times: M 9:30AM – 12:00PM

This seminar will examine philosophical issues connected to systems, cognitive and computational neuroscience. In particular we will focus on techniques (new and old) that neuroscientists have developed in order to handle the daunting complexity of neural systems, examining them in the light of the extensive philosophical literature on idealization, abstraction and minimal models. We will also look in detail at recent debates over the nature of explanation in neuroscience, comparing mechanist, dynamicist and other accounts. Examples of seminar themes are: The Neuron Doctrine; Efficient Coding Theories; Large-scale Neural Simulations; Canonical Computations and Networks.

Pitt Mathematics

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

  • Instructor: Brent Doiron
  • Location: Thackeray 525
  • Days/Times: M/W 9:30AM – 10:45AM

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 130
  • Days/Times: T/R 10:30AM – 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/MSNBIO 2100 Cellular and Molecular Neurobiology 1: CR HRS: 5.0 [CNBC core course]
NROSCI/MSNBIO 2101 Cellular and Molecular Neurobiology 2: CR HRS: 3.0[CNBC core course]

  • Instructor: Jon Johnson
  • Location: Victoria Building 116
  • Days/Time: M/T/R/F 9:00AM – 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 protein chemistry, regulation of gene expression, nerve cell biology, signal transduction, development, and neurogenesis in a lecture format.

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, signal propagation in nerve cells, and synaptic transmission.

Prerequisites: A background in basic biology and permission of the instructor are 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 adequate time to background reading might prefer to take Advanced Cellular Neuroscience instead.

Pitt Ophthalmology

INTBP 2100 Biology of Vision CR HRS: 2.0

  • Course Coordinators: Shiva Swamynathan, Rob Shanks, Matt Smith
  • Location: TBA
  • Days/Times: M/W 11:00AM – 11:50AM

‘Biology of Vision’ (INTBP2100) will introduce students to the basic biology of vision and vision-related research. Topics include: ocular anatomy and development; structure and function of the anterior segment; immunology and diseases of the eye; retinal structure, function and disease; imaging the visual system; visual perception; eye movements. The overall goal is to give students an understanding of the full range of vision research from a variety of methodological perspectives. The course is designed primarily for graduate students and post-doctoral fellows at Pitt and CMU, but interested advanced undergraduate students may contact one of the course coordinators for permission to register.

Pitt Psychology

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

  • Instructor: Scott Fraundorf
  • Location: Cathedral of Learning 142
  • Days/Times: M 10:30AM – 12:55PM

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 2476 Topics in Cognitive Psychology: Human Developmental Neuroscience: CR HRS: 3.0

  • Instructor: Melissa Libertus
  • Location: 9th floor, Learning Research and Development Center
  • Days/Times: M 9:00AM – 11:30AM

In this course, students will be introduced to the basic neuroscience techniques used to understand human brain development and how these can be applied to further our understanding of child development with a special emphasis on cognitive, social, and affective development. Neuroscientific methods surveyed will include MRI, fMRI, DTI, EEG/ERPs, MEG, NIRS, and lesion studies. Topic areas will include perception, language, memory, executive functions, numerical cognition, social cognition, and emotions. Clinical examples will be discussed as case studies to elucidate theories of development.

PSY 2575 Topics in Psychology: fMRI: CR HRS: 3.0

  • Instructor: Kirk Erickson
  • Location: Old Engineering Hall 303
  • Days/Times: M 3:00PM – 5:50PM

This is a lab course that will focus on analyzing functional MRI data. Students will learn how to take a data set collected using fMRI and analyze it, create figures, and describe the methods and results in writing. These steps will include data reconstruction, preprocessing, statistics, creating and analyzing regions-of-interest, and creating and generating figures and plots. The goal of the class is for students to learn how to analyze a basic fMRI data set. Students with an interest in understanding neuroimaging methods, but without much background, are encouraged to attend or email the instructor for more information.