This lesson provides an introduction to biologically detailed computational modelling of neural dynamics, including neuron membrane potential simulation and F-I curves.
In this lesson, users learn how to use MATLAB to build an adaptive exponential integrate and fire (AdEx) neuron model.
In this lesson, users learn about the practical differences between MATLAB scripts and functions, as well as how to embed their neuronal simulation into a callable function.
This lesson teaches users how to generate a frequency-current (F-I) curve, which describes the function that relates the net synaptic current (I) flowing into a neuron to its firing rate (F).
This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.
This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.
This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.
In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.
This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.
This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.
This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.
This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.
This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.
This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.