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. 

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. 

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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