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 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

This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).

Difficulty level: Intermediate

Duration: 1:43

Speaker: : Florence I. Kleberg

This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.

Difficulty level: Intermediate

Duration: 2:58

Speaker: : Florence I. Kleberg

In this lesson, you will learn about the intrinsic plasticity of single neurons.

Difficulty level: Intermediate

Duration: 2:08

Speaker: : Florence I. Kleberg

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

Difficulty level: Intermediate

Duration: 1:58

Speaker: : Florence I. Kleberg

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

Difficulty level: Intermediate

Duration: 1:40

Speaker: : Florence I. Kleberg

This lesson briefly wraps up the course on Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate

Duration: 0:37

Speaker: : Florence I. Kleberg

- Bayesian networks (2)
- (-) Cognitive neuroinformatics (1)
- Neuroimaging (15)
- Machine learning (2)
- Standards and best practices (4)
- (-) Tools (1)
- Repositories and science gateways (1)
- General neuroscience (6)
- Computational neuroscience (20)
- Statistics (4)
- Computer Science (1)
- Genomics (5)
- Data science (10)
- Open science (4)