Connectionists: Reminder: Application deadline April 30, for Berkeley course in mining and modeling of neuroscience data

[Jeff Teeters]

A reminder: the application deadline is in about 6 days (end of April).
Information about the course is below.

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We invite applicants to the 2017 summer course in
"Mining and modeling of neuroscience data"
to be held July 10-21 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 10-21, 2017
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.

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
Frederic Theunissen, University of California Berkeley
Jonathan Pillow, Princeton University
Odelia Schwartz, University of Miami
Mark Goldman, University of California Davis
Maneesh Sahani, Gatsby Unit, University College London

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
economy travel expenses (up to a maximum of $500 domestic, $600 foreign)
and will have most meals provided.

Housing
Rooms in the University dorms will be provided for those non-local
attendees who need accommodations. Most dorm rooms are double occupancy
(shared).

Food
Breakfast and some dinners will be provided to all participants as a group.
Non-local attendees will also be provided most other meals in the
University dining commons.

How to apply
To apply, submit the online form http://crcns.org/course/apply.php. 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
-    Overview of stimulus-response function models
-    Theory of model fitting / regularization / hypothesis testing
-    Bayesian methods
-    Estimation of stimulus-response functionals:  regression methods,
spike-triggered covariance
-    Variance analysis of neural response
-    Estimation of SNR. Coherence
-    Generalized Linear Models
Information theoretic approaches:
-    Information transmission rates and maximally informative dimensions
-    Scene statistics approaches and neural modeling
Techniques for analyzing multiple-unit 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
Models of memory, motor control and decision making:
-    Neural integrators
-    Attractor networks
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