Course:

This lesson provides an introduction to biologically detailed computational modelling of neural dynamics, including neuron membrane potential simulation and F-I curves.

Difficulty level: Intermediate

Duration: 8:21

Speaker: : Mike X. Cohen

Course:

In this lesson, users learn how to use MATLAB to build an adaptive exponential integrate and fire (AdEx) neuron model.

Difficulty level: Intermediate

Duration: 22:01

Speaker: : Mike X. Cohen

Course:

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.

Difficulty level: Intermediate

Duration: 11:20

Speaker: : Mike X. Cohen

Course:

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).

Difficulty level: Intermediate

Duration: 20:39

Speaker: : Mike X. Cohen

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

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.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

Course:

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.

Difficulty level: Intermediate

Duration: 1:16:10

Speaker: : John Griffiths

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:51

Speaker: : Dan Goodman

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.

Difficulty level: Intermediate

Duration: 10:01

This lesson provides a brief introduction to the *Computational Modeling of Neuronal Plasticity.*

Difficulty level: Intermediate

Duration: 0:40

Speaker: : Florence I. Kleberg

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

Difficulty level: Intermediate

Duration: 1:23

Speaker: : Florence I. Kleberg

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

Difficulty level: Intermediate

Duration: 1:20

Speaker: : Florence I. Kleberg

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

Difficulty level: Intermediate

Duration: 1:08

Speaker: : Florence I. Kleberg

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

Difficulty level: Intermediate

Duration: 1:18

Speaker: : Florence I. Kleberg

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

Difficulty level: Intermediate

Duration: 1:26

Speaker: : Florence I. Kleberg

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

Difficulty level: Intermediate

Duration: 0:42

Speaker: : Florence I. Kleberg

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

Difficulty level: Intermediate

Duration: 2:40

Speaker: : Florence I. Kleberg

This lesson reviews theoretical and mathematical descriptions of correlated spike trains.

Difficulty level: Intermediate

Duration: 2:54

Speaker: : Florence I. Kleberg

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