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

Course:

This demonstration walks through how to import your data into MATLAB.

Difficulty level: Beginner

Duration: 6:10

Speaker: : MATLAB®

Course:

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.

Difficulty level: Beginner

Duration: 15:10

Speaker: : MATLAB®

Course:

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

Difficulty level: Beginner

Duration: 2:49

Speaker: : MATLAB®

Course:

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

Difficulty level: Beginner

Duration: 6:10

Speaker: : MATLAB®

Course:

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

Difficulty level: Beginner

Duration: 6:27

Speaker: : MATLAB®

Course:

This brief tutorial goes over how you can easily work with big data as you would with any size of data.

Difficulty level: Beginner

Duration: 3:55

Speaker: : MATLAB®

Course:

In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

Difficulty level: Beginner

Duration: 3:52

Speaker: : MATLAB®

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

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