mikexcohen.com

In-silico teaching

Udemy courses: Specialized courses with video lectures, course readers, exercises and their solutions. Course topics include MATLAB programming, Fourier analysis, and linear algebra. The following courses are currently or will soon be available:

Learn MATLAB programming, debugging, and style. This online course teaches beginner and intermediate-level MATLAB programming. A major focus is on learning strategies to debug (find and fix coding errors) and develop your coding style.
TIP: Use this link for discounted registration!

Understand the Fourier transform and its applications. Learn the fundamental principles, subtle features, and major applications of the Fourier transform.
TIP: Use this link for discounted registration!

Simulate, understand, and visualize data. This course teaches how to simulate and characterize the myriad types of data, ranging from distributions to time series to images to clusters.
TIP: Use this link for discounted registration!

Learn linear algebra: theory and implementation (expected in spring of 2018!). Everything you need to know about linear algebra and its applications in signal processing, data analysis, and dimensionality reduction.

Online lecturelets: over 50 recorded lectures (mostly 20-30 minutes long), including downloadable Matlab code, on neural time series analysis.

Radboud Summer School The following two 5-day intensive courses will be offered in August 2018. Follow the links for more detailed information and application!
1. Course A: Analyzing neural time series data (6-10 August). Fourier transform, convolution, time-frequency analysis, synchronization, nonparametric statistics, simulating time series data. Here is the preliminary syllabus.
2. Course A2: EXTRA EDITION!!! Analyzing neural time series data (25-29 June). Due to popular demand, we've added a second offering of this course. See this pdf for more information!
3. Course B: Linear algebra for neuroscientists (13-17 August). Matrix algebra, least-squares model fitting, eigendecomposition, multivariate source separation, state-space analyses, simulating multicomponent and multichannal time series data. Here is the preliminary syllabus.
CuttingEEG conference (July 2018): workshop on methods for simulating EEG data to test analysis methods. Conference website will be linked when it becomes available (here is the website from the previous CuttingEEG).


		Mike X Cohen (CV) Lab PI Publications on Google-scholar. Likes to analyze data, talk science, look at purple, and eat dark chocolate.


		Piray Atsak (funded by own Veni) Assistant professor Publications on Google-scholar. Likes to study endocanabanoid system and development, and check out the music scene in NYC.


		Paul Anderson (funded by Hypatia) Postdoc Publications on Google-scholar. Likes to study the dopamine system, and listen to good music.


		Nils Zuiderveen Borgesius (funded by ERC) Postdoc Publications on Google-scholar. Likes to study the prefrontal-parietal-collicular system, and experiment with facial hair.


		Arthur Sergio Cavalcanti de França (funded by ERC) Postdoc Publications on Google-scholar. Likes to study SOM/PVB interneurons in cortico-cortical synchronization, and travel between warm and cold places (Brazil and Sweden).


		Sjef van Hulten (funded by Hypatia) Tech Likes to work with animals and build perfect equipment, study the dopamine system, and listen to good music.


		Nader Marzband (funded by Hypatia) PhD student Likes to study the dopamine system, and listen to Iranian music and cook Iranian food.


		Ashutosh Mishra (funded by ERC, co-supervised with Bernhard Englitz) PhD student Likes to analyze data and understand multidimensional networks, and play volleyball.


		Mitchel Stokkermans (funded by internal junior-rounds grant, co-supervised with Vivian Weerdesteyn) PhD student Likes to study human error detection and cognitive control, practise meditation, organize social events and is a funky/groovy house music DJ.


		Marrit Zuure (funded by ERC) PhD student Likes to build biophysical models of cortical circuits and apply information theory to understand neural representations, and tend to tarantulas and contemplate Oxford commas.


		Published by MIT Press. Also available via amazon.com amongst other sources. Table of contents MATLAB code Using the code without following the book may lead to confusion, incorrect data analyses, and misinterpretations of results. Mike X Cohen assumes no responsibility for inappropriate or incorrect use of this code. Click here to get the code (~217mb). Solutions to most exercises Obviously, I don't recommend cheating, but if you really insist on having the solutions, you can download them here. Note that there are many different ways of solving the same problem, so your solutions might look very different from mine while still being correct. Learn MATLAB programming, debugging, and style. This online course teaches beginner and intermediate-level MATLAB programming. A major focus is on learning strategies to debug (find and fix coding errors) and develop your coding style.


		Published by MIT Press. Also available via amazon.com amongst other sources. Table of contents MATLAB code Using the code without following the book may lead to confusion, incorrect data analyses, and misinterpretations of results. Mike X Cohen assumes no responsibility for inappropriate or incorrect use of this code. Click here to get the code (~22mb). Question-answer forum Contributed teaching material Dr. Jim Cavanagh has made his teaching material available on his google-drive. Professor John Allen has made his teaching material (much of it made by graduate students in his course) available on his website. Lyndon Duong is working on a Python translation of the Matlab code that accompanies the book. pdf versions of all of the figures available for download here (~80 MB). If you would like to link your teaching slides here, email me. Online lecturelets. >50 recorded lectures that explain and expand on the material presented in the book.


		Published by Sinc(X) Press and available via amazon.com as a kindle book (you don't need a kindle device to read the book). MATLAB code Using the code without following the book may lead to confusion, incorrect data analyses, and misinterpretations of results. Mike X Cohen assumes no responsibility for inappropriate or incorrect use of this code. Click here to get the code (~1mb).


		Published by Sinc(X) Press and available via amazon.com