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.