Connectionists: Videos of previous Berkeley course in mining and modeling neuroscience data

[Jeff Teeters]

Videos of the lectures given in the 2017 and 2016 Berkeley course in mining
and modeling neuroscience data are available online at:

2017 course:
https://archive.org/search.php?query=2017+crcns+course&sort=titleSorter

2016 course:
https://archive.org/search.php?query=2016+crcns+course&sort=titleSorter



On Wed, Mar 7, 2018 at 4:55 PM, Jeff Teeters <jteeters at berkeley.edu> wrote:

> We invite applicants to the 2018 summer course in
> "Mining and modeling of neuroscience data"
> to be held July 9-20 at UC Berkeley.
> A description of the course is below and also at:
> http://crcns.org/course
> Application deadline is April 20.
>
> -----
>
> Berkeley summer course in mining and modeling of neuroscience data
>
> July 9-20, 2018
> 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
> Sonja Grün, Juelich Research Center, Germany
> Frederic Theunissen, University of California Berkeley
> Odelia Schwartz, University of Miami
> Stephanie Palmer, University of Chicago
> 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 must 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 and $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
> Almost all meals will be provided to non-local attendees and some meals
> will be provided to local attendees.
>
> 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 April 20.   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
> - Population coding
> - Spike trains, firing rate, point processes, and generalized linear
> models
> - Statistical thinking in neuroscience
> - 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-channel recordings:
> - Unitary event analysis
> - Proper surrogates for spike synchrony analysis
> - Sparse coding/ICA methods, vanilla and methods including statistical
> models of nonlinear dependencies
> - Deep learning and supervised learning
> - Methods for assessing functional connectivity
> - Generalized Linear Models
> - Multivariate phase coupling
> - Statistical issues in network identification
> - Low-dimensional latent dynamical structure in network activity –
> Gaussian process factor analysis and newer approaches
> - Extracting population responses to experimental inputs - LDA, DPCA and
> other low-rank regression
>
>
>
>
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