This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:05

Speaker: : Dan Goodman

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.

Difficulty level: Intermediate

Duration: 5:58

Speaker: : Dan Goodman

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.

Difficulty level: Intermediate

Duration: 4:10

Speaker: : Dan Goodman

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.

Difficulty level: Intermediate

Duration: 3:54

Speaker: : Dan Goodman

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network.

Difficulty level: Intermediate

Duration: 9:40

Speaker: : Dan Goodman

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 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

- Artificial Intelligence (1)
- Notebooks (1)
- Provenance (1)
- DANDI archive (1)
- EBRAINS RI (6)
- Animal models (2)
- Brain-hardware interfaces (1)
- Clinical neuroscience (23)
- General neuroscience
(17)
- (-)
General neuroinformatics
(1)
- Computational neuroscience (83)
- Statistics (5)
- (-) Computer Science (5)
- (-) Genomics (8)
- Data science
(9)
- Open science (5)
- Project management (1)
- Education (1)
- Neuroethics (5)