From raphael.ritz at incf.org  Tue Mar  1 13:20:28 2011
From: raphael.ritz at incf.org (Raphael Ritz)
Date: Tue, 01 Mar 2011 13:20:28 +0100
Subject: [Incf-info] Training in Neuroinformatics - Call for Letters of
	Intent
Message-ID: <4D6CE48C.7090704@incf.org>

[sorry if you receive this through multiple channels]

The International Neuroinformatics Coordinating Facility (INCF; 
www.incf.org) is committed to support training in neuroinformatics. INCF 
now invites applications to run short courses lasting between two and 
seven days on any aspects of neuroinformatics at a level suitable for 
PhD students and beyond.  The courses should be held in English and open 
to any researcher interested in neuroinformatics. The INCF offers 
financial support to successful proposals, which would cover the venue, 
travel and accommodation costs for a small number of speakers and 
participants. Financial support from other sources is encouraged. One 
condition of funding is that the course material be made available 
publically, through INCF. Courses are envisaged to take place in 2012.

The Letter of Intent must include information about

1. Topic
2. Organizers
3. Target audience
4. Course description including justification (maximum 250 words)
5. Dissemination plan
6. Speakers - a preliminary list would be helpful
7. Course duration
8. Proposed dates and location
9. Estimated number of participants
10. Draft budget including the amount to be sought from the INCF

The closing date for Letters of Intent is 31st March 2011.  Letters 
should be sent in PDF format per email to training-proposals at incf.org.
The authors of the most promising Letters of Intent will be invited by 
25th April 2011 to submit a full proposal, with a deadline of 1st August 
2011.

Informal enquiries should be directed to Raphael Ritz 
(raphael.ritz at incf.org) on behalf of the INCF Training Committee.

http://www.incf.org/documents/training/LoI.pdf

Raphael

-- 
Dr. Raphael Ritz
Scientific Officer

International Neuroinformatics Coordinating Facility
Karolinska Institutet
Nobels v?g 15 A
SE-171 77  Stockholm
Sweden

Email: raphael.ritz at incf.org
Phone: +46 8 524 87017
Fax: +46 8 524 87150
web: www.incf.org

From fsommer at berkeley.edu  Sat Mar  5 02:25:17 2011
From: fsommer at berkeley.edu (Fritz Sommer)
Date: Fri, 4 Mar 2011 17:25:17 -0800
Subject: [Incf-info] Berkeley course in mining and modeling of neuroscience
	data, July 11-22, 2011
In-Reply-To: <AANLkTimW6uGkLZ3_3VapFupFCVox3dZ=QkqZwhTEPP=9@mail.gmail.com>
References: <AANLkTimW6uGkLZ3_3VapFupFCVox3dZ=QkqZwhTEPP=9@mail.gmail.com>
Message-ID: <7C649064-A7FC-4404-A739-15262B2B60A3@berkeley.edu>

Call for applications:
(Apologies for duplicate postings)

We invite applicants to this new 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 5.

Berkeley summer course in mining and modeling of neuroscience data
July 11-22, 2011
Redwood Center for Theoretical Neuroscience, UC Berkeley
Organizers:  Fritz Sommer, Jeff Teeters

Scope
This course addresses 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 the 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 partially sponsored by the National Science Foundation from
a grant supporting activities at CRCNS.org, which hosts a public
repository of experimental neuroscience data.

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
Sonja Gruen, Institute for Neuroscience and Medicine INM-6, Research
Center Juelich, Germany and RIKEN Brain Science Institute, Wako-Shi,
Japan
Robert Kass, Carnegie Mellon University, Pittsburgh
Jonathan Pillow, University of Texas, Austin
Maneesh Sahani, Gatsby Unit, University College London
Odelia Schwartz, Albert Einstein College of Medicine
Frederic Theunissen, University of California, Berkeley

Speakers
To complement the main course instruction there will be lectures by
other neuroscientists presenting their research using quantitative
approaches. These speakers, and their research areas are:  
Jose Carmena, UC Berkeley: Brain-machine interfaces (BMI)  
Yang Dan, UC Berkeley: Encoding and processing of visual information in the
mammalian brain
Walter Freeman, UC Berkeley: Developing dynamical theories of brain function
using recordings from high-density electrode arrays
Jack Gallant, UC Berkeley: Use of fMRI and other data to understand the human
visual system at a quantitative, computational level
Mark Goldman, UC Davis: Deducing operation of networks of large numbers of
interconnected neurons using single neuron measurements
Jennifer Linden, University College London: Structure and function of cortex and
sensory systems
Bin Yu, UC Berkeley: Statistical machine learning and methodologies involving
large data sets

Requirements
Applicants should be familiar with linear algebra, probability,
differential and integral calculus and have some experience using
MatLab or other software for performing interactive mathematical
computations (such as Python or Mathematica).  MatLab is
recommended because most exercises will be geared for MatLab.  Each
student should bring a laptop with the software installed.

Cost
$800 for tuition.  Room and board not included.  Financial assistance
may be available and must be requested on the application form.

Housing
Dorm housing is available.  The lowest rate is $384 for the entire two
weeks per person in a double occupancy room (about $27.50 per night).
Details: The room rate is $64 per night or $384 per week (seven
consecutive nights) for a single or double occupancy room.  Since the
price of a double occupancy room is the same if one or two people are
in it, sharing the room with someone will reduce the price per person
to one half of the above.  We will help coordinate sharing of rooms
for those who wish to do that.  Information about the dorm rooms is
at: http://conferenceservices.berkeley.edu/summervis_index.html

Food
Meals are available in the dorm cafeteria and in local restaurants.
They are not included with the course.

How to apply
To apply, fill out the form online linked from:
http://crcns.org/course.   The application is done entirely on-line.
A curriculum vitae and a letter of recommendation is required. The
course is limited to 20 students.

Application Deadline
Applications must be received by April 5.  Notifications of acceptance
will be given by the end of April.

Payment deadlines
If admitted, deposit of $300 must be made by May 9.  Remainder payment
for the course ($500) is due May 31.  If using dorm housing, to
guarantee a room, reservations must be made by May 31.  After that,
reservations may be made on space available basis.  Payment for
housing is made directly to the housing office when checking in (on
July 10).

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

Topics covered
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
-	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 and maximally informative dimensions
-	Scene statistics approaches and neural modeling
Techniques for analyzing multiple-unit recordings:
-	Cross-correlation and JPSTH
-	Sparse coding/ICA methods, vanilla and methods including statistical
models of nonlinear dependencies
-	Unitary event analysis
-	Proper surrogates for spike synchrony analysis
-	Methods for assessing functional connectivity
-	Advanced topics in generalized linear models
-	Low-dimensional latent dynamical structure in network activity ?
Gaussian process factor analysis and newer approaches


----------------------------------------------------------------------------
Friedrich T. Sommer, Ph.D.
Redwood Center for Theoretical Neuroscience &
Helen Wills Neuroscience Institute
UC Berkeley
575A Evans Hall, MC# 3198 
Berkeley, CA 94720-3198 
http://redwood.berkeley.edu/wiki/Fritz_Sommer


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