Connectionists: Berkeley course in mining and modeling neuroscience data, July 11-22, 2016

[Jeff Teeters]

Call for applications:

We invite applicants to the 2016 summer course in
"Mining and modeling of neuroscience data"
to be held July 11-22 at UC Berkeley.
A description of the course is below and also at:
http://crcns.org/course
Application deadline is April 30.

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Berkeley summer course in mining and modeling of neuroscience data

July 11-22, 2016
Redwood Center for Theoretical Neuroscience, UC Berkeley
Organizers: Fritz Sommer, Bruno Olshausen &
Jeff Teeters (HWNI, UC Berkeley)

Scope
This course is for students and researchers with backgrounds in mathematics
and computational sciences who are interested in applying their skills
toward problems in neuroscience.  It will introduce the major open
questions of neuroscience and teach state-of–the-art techniques for
analyzing and modeling neuroscience data sets.  The course is designed for
students at the graduate level and researchers with background in a
quantitative field such as engineering, mathematics, physics or computer
science who may or may not have a specific neuroscience background. The
goal of this summer course is to help researchers find new exciting
research areas and at the same time to strengthen quantitative expertise in
the field of neuroscience. The course is sponsored by the National
Institute of Health, the National Science Foundation from a grant
supporting activities at the data sharing repository CRCNS.org, and the
Helen Wills Neuroscience Institute at UC Berkeley.

Format
The course is “hands on” in that it will include exercises in how to use
and modify existing software tools and apply them to data sets, such as
those available in the CRCNS.org repository.

Course Instructors
Robert Kass, Carnegie Mellon University, Pittsburgh
Maneesh Sahani, Gatsby Unit, University College London
Odelia Schwartz, University of Miami
Frederic Theunissen, University of California Berkeley
Joshua Vogelstein, Johns Hopkins University

Course Moderators
Fritz Sommer and Jeff Teeters, Redwood Center for Theoretical Neuroscience.

Speakers
To complement the main course instruction there will be lectures in the
evenings by local Berkeley and UCSF neuroscientists presenting their
research using quantitative approaches.

Requirements
Applicants should be familiar with linear algebra, probability,
differential and integral calculus and have some experience using MatLab
and Python.  Each student should bring a laptop with both MatLab and Python
installed.

Cost
There is no cost to attend.  Non-local attendees will be reimbursed for
qualifying travel and food expenses if receipts are provided.

Housing
Rooms in a hotel within walking distance to campus will be provided for
those attendees who need accommodations.

Food
Breakfast and some dinners will be provided to all participants as a
group.  Non-local attendees can purchase food at local stores or
restaurants and be reimbursed.

How to apply
To apply, submit the online form linked from: http://crcns.org/course.   A
curriculum vitae and a letter of recommendation are required.  The course
is limited to 25 students.

Deadlines
Applications must be received by the end of April.   Notifications of
acceptance will be given by May 15.

Questions
Questions about the course can be sent to course [at] crcns.org.

Topics covered (subject to change):
Basic approaches:
- The problem of neural coding
- Spike trains, point processes, and firing rate
- Statistical thinking in neuroscience
- Theory of model fitting / regularization / hypothesis testing
- Overview of stimulus-response function models
- Theory of model fitting / regularization / hypothesis testing
- Bayesian methods
- Spike sorting
- Estimation of stimulus-response functionals:  regression methods,
spike-triggered covariance,
- Variance analysis of neural response
- Estimation of SNR. Coherence
Information theoretic approaches:
- Information transmission rates
- Scene statistics approaches and neural modeling
Techniques for analyzing multiple-electrode and optical neural recordings:
- Event sorting in electrophysiology and optical imaging
- Optophysiology cell detection
- Sparse coding/ICA methods, vanilla and methods including statistical
models of nonlinear dependencies
- Methods for assessing functional connectivity
- Statistical issues in network identification
- Low-dimensional latent dynamical structure in network activity–Gaussian
process factor analysis/newer methods
Analyzing structural data:
- Histology cell detection
- Statistical connectomics
Neuroinformatics topics:
- Petascale data storage, exploration, parsing and analysis
- Introduction into Neurodata Without Borders data format for
Neurophysiology
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