Computational Neuroscience Workshop 2014 – Online and Timeless Material

Hello everyone!

From the feedback I had over the last two weeks and my own feelings, force is to conclude that this year again the workshop has been an exceptional occasion to acquire and share information on computational tools available to neuroscientists.

I will summarize in one sentence so catch up your breath: in the morning we had a talk on normal distribution statistics and a second on support vector machines, the early afternoon block followed with a talk on circular statistics and one on non-linear system identification; finally the day ended with small scale neural population modeling and functional clustering and an excellent and comprehensive description of a non-parametric approach to non-linear system modeling using Volterra series. How did that sound to you?

One thing for sure each one of these talks covered a pertinent and actual topic, ranging from beginning to expert level, but this is not all of it!

Like last year, we are concerned in providing a timeless dimension to the workshop. Youtube providing us with a fantastic platform to reach a greater audience, each talks have been edited and posted online. Through this, anyone in the world will be free to benefit from the excellent work of our speakers. In addition, speakers were invited to publish code samples related to their talk, in order for anyone to quickly get started with their computational tool of choice. The listing below will guide you through these online supplements to the workshop experiment.

Before leaving it up to you to explore these online documents, let me take the time to thank everyone who have been involved in the workshop, first of all, the speakers without whom there wouldn’t be a workshop: Kelly Bullock, Sébastien Tremblay, Nour Malek, Adam Schneider, Richard Greg Stacey, Lennart Hilbert and Theodore Zanos. Second but not less importantly, these people have helped me during the workshop by taking notes, taking pictures, moving tables around, something that I couldn’t have done alone, a big thank you: Nathan Friedman, Matthew Krause, Nour Malek. And finally, students and others who have attended the workshop, we did it for you, and you presence encourages us in continuing, thank you for taking the time to join us in this project.

See you next year,

Frédéric Simard

Listing of the talks:

This video presents the basis of normal distribution statistics: the standard normal distribution, tests for normality, skewness and kurtosis and unpaired and paired test on the mean.

This video presents the support vector machines in the context of computational neuroscience. Used for population code analysis, it contains a comprehensive description of this algorithm and how to get started.

You can find a self-contained code sample here:

This video presents the basis of circular statistics and is framed around the toolbox of Philipp Berens. It contains the description of the Von Mises probability density distribution, parametric and non-parametric tests for uniformity, on the mean, on the median and others. All in the context of computational neuroscience.

You can find a self-contained code sample here:

This video discuss linear and on-linear systems properties and identification. It covers non-linear system cascade modeling using reverse correlation or spike triggered average.

A video about small-scale neural network modeling. It focuses on the identification of synchronicity in the neural population.

This talk follows up Greg Stacey’s talk and covers the interesting topic of functional clustering, through hierarchical clustering, of synchronous neurons.

A description of Volterra Series and how they can be used to model non-linear systems. It is a powerful and non-parametric approach that rely, in this version, on Laguerre expansion functions. An advanced topic made accessible by Theodore Zanos.

You can find a self-contained code sample here:

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