This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, *Understanding Neural Networks*.

Difficulty level: Intermediate

Duration: 2:43

Speaker: : Marcus Ghosh

Course:

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package.

Difficulty level: Intermediate

Duration: 2:22:28

Speaker: : Jake Vanderplas

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology.

Difficulty level: Intermediate

Duration: 33:41

Speaker: : Yaroslav O. Halchenko

This lesson continues from part one of the lecture *Ontologies, Databases, and Standards*, diving deeper into a description of ontologies and knowledg graphs.

Difficulty level: Intermediate

Duration: 50:18

Speaker: : Jeff Grethe

This lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate

Duration: 21:54

Speaker: : Martin Hofmann-Apitius

Course:

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.

Difficulty level: Intermediate

Duration: 1:26:06

Speaker: : Bard Ermentrout

Course:

This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons.

Difficulty level: Intermediate

Duration: 1:25:38

Speaker: : Bard Ermentrout

Course:

In this lesson you will learn about fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks.

Difficulty level: Intermediate

Duration: 1:24:30

Speaker: : Bard Ermentrout

Course:

This lesson continues discussing properties of neural oscillations and networks.

Difficulty level: Intermediate

Duration: 1:31:57

Speaker: : Bard Ermentrout

Course:

In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

Difficulty level: Intermediate

Duration: 1:26:02

Speaker: : Bard Ermentrout

Course:

This lesson provides a continued discussion and characterization of coupled oscillators.

Difficulty level: Intermediate

Duration: 1:24:44

Speaker: : Bard Ermentrout

Course:

This lesson gives an overview of modeling neurons based on firing rate.

Difficulty level: Intermediate

Duration: 1:26:42

Speaker: : Bard Ermentrout

Course:

This lesson characterizes the pattern generation observed in visual system hallucinations.

Difficulty level: Intermediate

Duration: 1:20:42

Speaker: : Bard Ermentrout

This lesson gives an introduction to stability analysis of neural models.

Difficulty level: Intermediate

Duration: 1:26:06

Speaker: : Bard Ermentrout

This lesson continues from the previous lectures, providing introduction to stability analysis of neural models.

Difficulty level: Intermediate

Duration: 1:25:38

Speaker: : Bard Ermentrout

In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.

Difficulty level: Intermediate

Duration: 1:24:30

Speaker: : Bard Ermentrout

This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting.

Difficulty level: Intermediate

Duration: 1:31:57

Speaker: : Bard Ermentrout

This lesson provides more context around weakly coupled oscillators.

Difficulty level: Intermediate

Duration: 1:26:02

Speaker: : Bard Ermentrout

This lesson builds upon previous lectures in this series, providing an overview of coupled oscillators.

Difficulty level: Intermediate

Duration: 1:24:44

Speaker: : Bard Ermentrout

In this lesson, you will learn about neuronal models based on their spike rate.

Difficulty level: Intermediate

Duration: 1:26:42

Speaker: : Bard Ermentrout

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