Official
course description: The
primary focus of this course
is the physiological basis of
behavior from a systems
perspective. Such topics as
localization of function,
neural models, and the
physiological bases of
sensory/motor systems,
learning/memory, and spatial
cognition are considered. The
laboratory introduces the
student to the anatomy and
physiology of the mammalian
central nervous system and to
some of the principal
techniques used in systems and
behavioral neuroscience.
Laboratory sections will be
assigned during the first week
of class.
Official
course description: This
course explores the
physiological basis and
functional relevance of
oscillations, which are
ubiquitous in the
brain. Rhythmic pattern
generators in specific neurons
and circuits are essential for
generating repeating movements
such as breathing and walking;
yet, oscillations are equally
prominent in neural systems
for sensation, cognition, and
memory. Could it be that these
rhythms are a fundamental
building block of information
processing in neural circuits?
This course provides an
introduction to the detection,
analysis and interpretation of
oscillations in the
brain. Using these tools, we
will survey the origin and
functional role of
oscillations in a variety of
neural systems across animal
and human species, and ask
what general principles
emerge.
This class
features "heads-on" experience
with headsets that record brain activity.
graduate
Analysis of
Neural Data
Official
course description:
Overall, the course is
designed to provide hands-on
experience with management,
visualization, and analysis of
neural data. Becoming skilled
at these things is a
rate-limiting step for many
graduate projects requiring
analysis. Even if your work
only requires rudimentary
analysis, awareness of what
else can be done and how to do
it well is valuable, for
instance when evaluating the
work of others in the
literature!
To do so, the focus is on introducing some commonly used tools, such
as GitHub and relevant functionality within MATLAB - and then to
actually use these on real data sets. Initially, those data sets will
be local field potentials and spiking data recorded from various brain
areas in freely moving rodents in the van der Meer lab; however, a
crucial goal of the course is that after some initial steps you will
use your own data. (If you don't have your own, you can do everything
with data provided here.)
We will make contact with a few concepts from computer science, signal
processing, and statistics. However, the focus is on making initial
steps that work and getting pointers to more complete treatment,
rather than a thorough theoretical grounding. Nevertheless, to make
sure that what you learn is not tied to specific data sets only, a
number of principles of data analysis - applicable to any project of
sufficient complexity - will be referenced throughout the course. You
are invited to think of these and others, not only as you progress
through this course, but especially as you organize your own data
analyses and read analyses performed by others.
This class uses
step-by-step tutorials, available
on
our lab
wiki.
Current Topics
in Behavioral Neuroscience
Official
course description:
Examining what changes in
behavior result from
alterations to structures in
the brain continues to be a
foundational approach in
neuroscience. The last decade
has seen an unprecedented
increase in the specificity of
such interventions; yet, these
technological advances have
placed in sharp focus the
difficulties inherent in
interpreting the resulting
behavioral effects. This
course provides training in
the design and interpretation
of contemporary behavioral
neuroscience
experiments. Topics include
multiple parallel systems,
compensation, learning
vs. performance, on-target
vs. off-target effects,
averaging across trials and/or
subjects, single vs. multiple
tasks, and functional
localization vs. distribution,
with particular focus on how
these perennial issues relate
to current and emerging
experimental tools.