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This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

Difficulty level: Beginner
Duration: 8:23
Speaker: : Geoffrey Hinton

This lesson provides an introduction to simple spiking neuron models.

Difficulty level: Beginner
Duration: 48 Slides
Speaker: : Zubin Bhuyan

This lesson provides an overview of plasticity on many levels, including short-term, long-term, metaplasticity, and structural plasticity. The lesson also provides xamples related to modelling of biochemical networks. 

Note: The sound uptake is a bit noisy the first few minutes, but gets better from about 5 mins in

Difficulty level: Beginner
Duration: 1:11:29
Speaker: : Upi Bhalla

This lesson gives an introduction to the modelling of chemical computation in the brain.

Difficulty level: Beginner
Duration: 1:00:11
Speaker: : Upi Bhalla

This lesson provides an introduction to the role of models in theoretical neuroscience.

Difficulty level: Beginner
Duration: 19:26
Speaker: : Jakob Macke

This lesson introduces different types of models, model complexity, and how to choose an appropriate model.

Difficulty level: Beginner
Duration: 39:09
Speaker: : Astrid Prinz

This lesson gives an overview of balanced excitatory-inhibitory (E-I) networks, stability, and gain modulation.

Difficulty level: Beginner
Duration: 1:22:11
Speaker: : Kenneth Miller

In this lesson, you will learn about methods for dimensionality reduction of data, with a focus on factor analysis.

Difficulty level: Beginner
Duration: 1:16:47
Speaker: : Byron Yu

This lesson gives an in-depth look into various types of neuronal networks, as well properties, parameters, and phenomena which characterize them. 

Difficulty level: Beginner
Duration: 1:39:32

In this lesson, you will learn about spiking neuron networks and linear response models.

Difficulty level: Beginner
Duration: 1:24:22

This lesson discusses Bayesian neuron models and parameter estimation.

Difficulty level: Beginner
Duration: 1:12:38
Speaker: : Jakob Macke

This lesson gives an overview of Bayesian memory and learning, as well as how to go from observations to latent variables.

Difficulty level: Beginner
Duration: 1:33:34
Speaker: : Máté Lengyel

In this lesson, you will learn about how constraints can help us understand how the brain works.

Difficulty level: Beginner
Duration: 1:34:42
Speaker: : Simon Laughlin

This lesson discusses how to approach neural systems from an evolutionary perspective.

Difficulty level: Beginner
Duration: 1:29:38
Speaker: : Gilles Laurent

This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

Difficulty level: Beginner
Duration: 7:57
Speaker: : Barton Poulson

This lesson recaps why math, in a number of ways, is extremely useful in data science.

Difficulty level: Beginner
Duration: 1:35
Speaker: : Barton Poulson

This lesson provides an introduction to the lessons in this course that deal with statistics and why they are useful for data science.

Difficulty level: Beginner
Duration: 4:01
Speaker: : Barton Poulson

In this lesson, users will learn about the importance of exploratory analysis, as well as how statistics enables one to become familiar with and understand one's data. 

Difficulty level: Beginner
Duration: 2:23
Speaker: : Barton Poulson

This lesson goes over graphical data exploration, including motivations for its use as well as practical examples of visualizing data distributions. 

Difficulty level: Beginner
Duration: 8:01
Speaker: : Barton Poulson

In this lesson, users learn about exploratory statistics, and are introduced to various methods for numerical data exploration.

Difficulty level: Beginner
Duration: 5:05
Speaker: : Barton Poulson