First day of classes: Pitt January 5, 2009; CMU January 12, 2009.
students in the CNBC graduate training program automatically have
instructor permission to attend any of these core courses, but cross-registration procedures may apply.
CMU Biological Sciences
[CNBC Core Course]
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.
03-815 Magnetic Resonance Imaging in Neuroscience: 9 units
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 Computer Science
15-685 Computer Vision: 12 units
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, but
biological and psychological perspectives will also be considered.
Topics covered include image formation and representation, multi-scale
analysis, segmentation, contour and region analysis, reconstruction of
depth based on stereo, texture shading and motion, and analysis and
recognition of objects and scenes using statistical and model-based
10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)
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.
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.
for 15-781 is restricted to CSD PhD and MS students only. All others
wishing to register should use the number 10-701, which is the home
number for the Machine Learning course.
85-712 Cognitive Modeling: 9 units
course will be concerned with modeling of agent behavior in a range of
applications from laboratory experiments on human cognition,
high-performance simulations such as flight simulators, and video game
environments like Unreal Tournament. The first half of the course will
teach a high-level modeling language for simulating human perception,
cognition, and action. The second half of the course will be a project
in which students develop a simulated agent or agents for the
application of their choice.
85-714 Cognitive Neuropsychology: 9 units
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?
course will provide an overview of parallel-distributed processing
models of aspects of perception, memory, language, knowledge
representation, 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.
85-723 Cognitive Development: 9 units
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.
85-726 Learning in Humans and Machines: 9 units
This course provides an introduction to probabilistic models of cognition.
The focus is on principles that can help to explain human learning and to
develop intelligent machines. Topics discussed will include
categorization, causal learning, language acquisition, and inductive
reasoning. Basic programming skills will be required for the problem sets.
85-729 Cognitive Brain Imaging: 9 units
seminar will examine how the brain executes higher level cognitive
processes, such as problem-solving, language comprehension, and visual
thinking. The topic will be addressed by examining what recent brain
imaging studies can tell us about these various kinds of thinking. This
new scientific approach has the potential of providing important
information about how the brain thinks, indicating not only what parts
perform what function, but also how the activity of different parts of
the brain are organized to perform some thinking task, and how various
neurological diseases (e.g. aphasia, Alzheimer’s) affect brain
activity. A variety of different types of thinking will be examined,
including short-term working memory storage and computation, problem
solving, language comprehension, visual thinking. Several different
technologies for measuring brain activity (e.g. PET and functional MRI
and also some PET imaging) will be c onsidered, attempting to relate
brain physiology to cognitive functioning. The course will examine
brain imaging in normal subjects and in people with various kinds of
brain damage. Graduate Students Only.
85-795 Applications of Cognitive Science: 9 units
famous psychologist George Miller once said that Psychology should
“give itself away.” The goal of this course is to look at cases where
we have done so — or at least tried. The course focuses on
applications that are sufficiently advanced as to have made an impact
outside of the research field per se. That impact can take the form of
a product, a change in practice, or a legal statute. The application
should have a theoretical base, as contrasted, say, with pure
measurement research as in ergonomics. Examples of applications are
virtual reality (in vision, hearing, and touch), cognitive tutors based
on models of cognitive processing, phonologically based reading
programs, latent semantic analysis applications to writing assessment,
and measurses of consumers’ implicit attitudes.
The course will use a case-study approach that considers a set of
applications in detail, while building a general understanding of what
it means to move research into the applied setting. The questions to be
considered include: What makes a body of theoretically based research
applicable? What is the pathway from laboratory to practice? What are
the barriers – economic, legal, entrenched belief or practice? The
format will emphasize analysis and discussion by students.
85-803 Computational Models of Normal and Disordered Cognition: 9 units
This seminar has as its goal to share ideas about computational modeling of cognition and cognitive neuroscience. This semester we plan to include a focus on exploring ways to develop computational models of disordered cognition and emotion as well as normal cognition. In past semesters (this seminar is held for one semester every other year), a number of faculty members, postdoctoral fellows and researchers as well as graduate students attended these seminars whether or not they were officially part of the training grant on computational modeling. Anyone interested in participating is welcome. Anyone who wishes to enroll for credit should contact Lynne Reder first. If you are not enrolled but wish to attend, please email me so that you can be included on the mailing list.
16-725 Medical Image Analysis: 12 units
(Cross-listed as Pitt Bioengineering BIOE 2630: Methods in Image Analysis.)
fundamentals of computational medical image analysis will be explored,
leading to current research in applying geometry and statistics to
segmentation, registration, visualization, and image understanding.
Student will develop practical experience through projects using the
National Library of Medicine Insight Toolkit ( ITK ), a new software
library developed by a consortium of institutions including the
University of Pittsburgh. In addition to image analysis, the course
will describe the major medical imaging modalities and include
interaction with practicing radiologists at UPMC.
36-702 Statistical Machine Learning CR: 12 units
Description not currently available
BIOE 2540 Neural Biomaterials and Tissue Engineering CR HRS: 3.0
course is designed to acquaint students with a understanding of
biomaterials and biocompatibility of various neural implants while also
discussing current approaches and theories in neural tissue engineering
BIOE 2696 Control Theory Neuroscience CR HRS: 3.0
Control theory has been an important tool for understanding the
organization and operation of the nervous systems. This course
introduces the general principles of control theory and its application
in neuroscience. Topics include: signals and systems through Fourier
transform; block diagrams and transfer functions, Laplace transform,
state-space description, system responses; phase-lead and phase-lag
compensators, PID controllers, theory of optimal control; Introduction
to the brain: cortex, cerebellum, brainstem, spinal cord; oculomotor
control: saccades,VOR, and smooth pursuit; arm movement control: loads,
redundant DOF, learning, internal models; human postural control.
NROSCI 2035 Control of Movement CR HRS: 3.0
This course will discuss the neural control of our actions in detail,
including planning of movement in the cortex, relay of motor commands
to the brainstem and spinal cord, coordination of movement by the
cerebellum and basal ganglia, adjustment of movement via brainstem
and spinal cord reflexes, execution of movement through contraction
of muscle fibers, and feedback about movement as mediated by
corollary discharge circuits. The focus will be on basic science,
supplemented by reviews of clinical issues. Course format will
include lectures and discussions of original research papers.
[CNBC Core Course]
course is a component of the introductory graduate sequence designed to
provide an overview of neuroscience. This course provides an
introduction to the structure of the mammalian nervous system and to
the functional organization of sensory systems, motor systems,
regulatory systems, and systems involved in higher brain functions. It
is taught primarily in a lecture format with some laboratory work. The
course covers in detail the major sensory, motor and behavioral
regulatory systems of the brain. The course satisfies the CNBC core
requirement in neuroanatomy.
PSY 2476 Brain Connectivity Mapping: 3 units
This class examines new non-invasive MRI imaging techniques and results for
creating a Human Connectome (map of all the brain areas their volume,
functional specialization, and their interconnectivity). These
novel/advanced techniques will advance the study of brain systems,
disorders, development, training, and neurosurgery. We will examine the
methods, and results of techniques available in Pittsburgh for imaging and
quantifying anatomical and functional connectivity. This class will review
the current literature on imaging based brain segmentation and tractography.
Students will learn to use of modern connection tracking software that will
be used in student projects. Graduate students are expected to do a
Connectome mini-project using the techniques on data that will be collected
during the course. The course covers the technology of structural,
functional, and connectivity imaging of both grey matter and white matter.
It will examine brain networks (sensory, executive, memory, affect, resting
state) and cortical and sub cortical systems. For additional details see
http://schneider.lrdc.pitt.edu/BCM/ . The class will meet Thursdays
3:00-5:30 PM room 303 Engineering Hall for both lectures and laboratory