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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

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

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

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 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 video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

Difficulty level: Intermediate
Duration: 2:37
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

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

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

Difficulty level: Intermediate
Duration: 1:20

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

Difficulty level: Intermediate
Duration: 1:08

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

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

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

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

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

Difficulty level: Intermediate
Duration: 2:54

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

Difficulty level: Intermediate
Duration: 1:43

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

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

Difficulty level: Intermediate
Duration: 2:08