Fall 2016

First day of classes: Monday, August 29, 2016

CNBC Core courses:

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

Statistical Models of the Brain, 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 & Claire Cheetham
  • Days/Times: T/R 9:00AM – 10:20AM (Doherty Hall 2302), T 3:30PM – 4:50PM (MI 130)

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.

86-717 Cognitive Neuropsychology : 9 units
(Cross listed as 85-714)

  • Instructor: Marlene Behrmann
  • Date/Time: MW 10:30AM – 11:50AM
  • Location: TBA

This course will review what has been learned of the neural bases of cognition through studies of brain-damaged patients as well as newer techniques such as brain stimulation mapping, regional metabolic and blood flow imaging, and attempt to relate these clinical and physiological data to theories of the mind cast in information-processing terms. The course will be organized into units corresponding to the traditionally-defined subfields of cognitive psychology such as perception, memory and language. In each area, we will ask: To what extent do the neurological phenomena make contact with the available cognitive theories? When they do, what are their implications for these theories (i.e., Can we confirm or disconfirm particular cognitive theories using neurological data?)? When they do not, what does this tell us about the parses of the mind imposed by the theories and methodologies of cognitive psychology and neuropsychology?

CMU Machine Learning

10-701 Introduction to Machine Learning: 12 units

  • Instructors: Poe Xing & Matthew Gormley
  • Location: Wean Hall 7500
  • Days/Times: M/W 3:00PM – 4:20PM

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 learn 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, mathematics and algorithms needed to do research and applications in machine learning. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. You can evaluate your ability to take the course via a self-assessment exam that will be made available to you after you register. If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the masters level course on Machine Learning, 10-601. This class may be appropriate for MS and undergrad students who are interested in the theory and algorithms behind ML. If you are unsure whether you have sufficient mathematical background to do well in this course, you should consider taking the mini 10-600 Mathematical Background for Machine Learning. You can evaluate your ability to take the course via a self-assessment exam at: https://qna-app.appspot.com/view.html?aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgICgpO-KCgw

ML course comparison: https://docs.google.com/document/d/1Y0Jx_tcINWQrWJx31WGEQSsUs059OUMmPIVSeyxNdeM/edit

CMU Psychology

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: 12 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 & Yaser Sheikh
  • 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 225B
  • Days/Times: M/W/F 1:30PM – 2:20PM

This is a course in data analysis using multiple 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

  • Instructor: Olga Chilina
  • Location: Lecture – Doherty Hall 2210, 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

36-759 Statistical Models of the Brain: 12 units  [CNBC core course] Cross-listed as Pitt MATH 3375

  • Instructor: Rob Kass
  • Location: Mellon Institute 130
  • Days/Times: W/F 1:30PM – 2:50PM

This course is intended for CNBC students, as an additional option for fulfilling the computational core course requirement, but it will also be open to Statistics and Machine Learning students. It should be of interest to anyone wishing to see the way statistical ideas play out within the brain sciences, and it will provide a series of case studies on the role of stochastic models in scientific investigation. Statistical ideas have been part of neurophysiology and the brainsciences since the first stochastic description of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago. Many contemporary theories of neural system behavior are built with statistical models. For example, integrate-and-fire neurons are usually assumed to be driven in part by stochastic noise; the role of spike timing involves the distinction between Poisson and non-Poisson neurons; and oscillations are characterized by decomposing variation into frequency-based components. In the visual system, V1 simple cells are often described using linear-nonlinear Poisson models; in the motor system, neural response may involve direction tuning; and CA1 hippocampal receptive field plasticity has been characterized using dynamic place models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities; and some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models incorporating random “noise components are used as an effective vehicle for data analysis. In neuroscience, however, the models also help form a conceptual framework for understanding neural function. This course will examine some of the most important methods and claims that have come from applying statistical thinking

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.

Pitt History and Philosophy of Science

HPS 2633 Philosophy of Cognitive Science CR HRS: 3.0

  • Instructor: Edouard Machery
  • Location: Cathedral of Learning G28
  • Days/Times: W 9:30AM – 12:00PM

This course will survey the main philosophical questions provoked by cognitive science. Students will acquire a comprehensive grasp of the main issues in this field. We will discuss questions such as: is the mind modular? Is the mind embodied and situated? Do we ascribe mental states by simulation or by means of a theory? What is consciousness?

Pitt Mathematics

MATH 3375 Computational Neuroscience Methods CR HRS: 3.0 [CNBC core course]
Cross-listed as CMU 36-759

  • Instructor: Brent Doiron
  • Location: MI 130
  • Days/Times: W/F 1:30PM – 2:50PM

This course will present the fundamentals of neural modeling, with a focus on establishing the computations performed by single neurons and networks of neurons. The aim of the course is to provide students with the necessary knowledge and toolbox from which to simulate neural dynamics within the context of a processing task. Topics to be covered include Hodgkin-Huxley model of a neuron, dendritic integration, reduced neuron models, modeling synaptic dynamics, behavior of small networks of neurons, Weiner analysis of a spike train, spike train statistics, information theory applied to neural ensembles.

Pitt Neuroscience

NROSCI 2005 Cognitive Neuroscience CR HRS: 3.0 [CNBC core course]
Cross-listed as CMU 85-765

  • Instructor: Carl Olson
  • 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 Interdisciplinary Biomed Grad Program

INTBP 2100 Biology of Vision CR HRS: 2.0

  • Course Coordinators: Shiva Swamynathan, Rob Shanks, Matt Smith, Kyle McKenna
  • 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 & Aleksandra Petkova
  • Location: Cathedral of Learning 142
  • Days/Times: M 1:00 PM – 3:55 PM

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 2471 Mapping Human Brain Connectivity: CR HRS: 3.0

  • Instructor: Walter Schneider
  • Location: Old Engineering Hall 303
  • Days/Times: T/R 4:00PM – 5:50PM

This class will cover background and technical methods of mapping high definition fiber tracking of brain connectivity for basic research and clinical imaging. Students will learn to map/quantify anatomical connections of the human brain. These techniques are used to study brain systems, disorders, and development, and to assist in planning neurosurgery. Students may take an optional one-credit laboratory in which they will learn to use advanced computation software to execute research projects including developing technical methods, mapping brain networks, or clinical analysis of data.