Essentials of Neuroscience with MATLAB.

Please watch the following intro video before starting the course.

Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Module 1, video 1: Background and goals


Module 1, video 2: Import and convert data


Module 1, video 3: Spike counts histograms


Module 1, video 4: Tuning curves


Module 1, video 5: Spatial map of spike counts


Module 1, video 6: Spatial map of tuning

Module 2: EEG

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Module 2, video 1: Background and goals


Module 2, video 2: Electrode positions in 2D and 3D


Module 2, video 3: Spectral analysis via FFT


Module 2, video 4: Spectral power from all channels


Module 2, video 5: Topographical maps


Module 2, video 6: Endogenous alpha


Module 2, video 7: Correlate alpha with SSVEP

Module 3: Model

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

Module 3, video 1: Background and goals


Module 3, video 2: Introduction to MATLAB live scripts


Module 3, video 3: Build the AdEx model


Module 3, video 4: Embed the simulation in a function


Module 3, video 5: Experiment: Generate an FI curve

Module 4: FMRI

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

Module 4, video 1: Background and goals


Module 4, video 2: Visualize flatmaps


Module 4, video 3: Preprocess BOLD signal data


Module 4, video 4: Trial-averaged BOLD matrix


Module 4, video 5: Animation of BOLD responses over time


Module 4, video 6: Event-related BOLD time courses


Module 4, video 7: T-test on condition differences

Module 5: Ca+ imaging

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholed spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image procsesing toolbox, and PCA.

Module 5, video 1: Background and goals


Module 5, video 2: Animate calicum fluctuations over time


Module 5, video 3: Convert data from cell to matrix


Module 5, video 4: Image processing to reduce background noise


Module 5, video 5: Identify cell bodies based on contiguity


Module 5, video 6: High-pass filer the time series data


Module 5, video 7: Compute and visualize a PCA

Dr. Mike X Cohen

Mike is an associate professor of neuroscience at the Donders Institute (Radboud University Medical Centre) in the Netherlands. He has over 20 years experience teaching scientific coding, data analysis, statistics, and related topics, and has authored several online courses and textbooks. He has a suspiciously dry sense of humor and apparently enjoys making pop-art in his spare time.