Course:

Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.

Neuronify aims to provide a low entry point to simulation-based neuroscience.

Difficulty level: Beginner

Duration: 01:25

Speaker: : Neuronify

This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.

Difficulty level: Beginner

Duration: 1:32:17

Speaker: : Shih-Chii Liu

Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.

Difficulty level: Beginner

Duration: 21:15

Speaker: : Tadashi Yamazaki

Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.

Difficulty level: Beginner

Duration: 41:57

Speaker: : Giacomo Indiveri

Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.

Difficulty level: Beginner

Duration: 20:39

Speaker: : Giacomo Indiveri

The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.

Difficulty level: Intermediate

Duration: 19:57

Speaker: : Runchun Mark Wang

Course:

Introduction to the Mathematics chapter of Datalabcc's "Foundations in Data Science" series.

Difficulty level: Beginner

Duration: 2:53

Speaker: : Barton Poulson

Course:

Primer on elementary algebra

Difficulty level: Beginner

Duration: 3:03

Speaker: : Barton Poulson

Course:

Primer on systems of linear equations

Difficulty level: Beginner

Duration: 5:24

Speaker: : Barton Poulson

Course:

How calculus relates to optimization

Difficulty level: Beginner

Duration: 8:43

Speaker: : Barton Poulson

Serving as good refresher, Shawn Grooms explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

Difficulty level: Beginner

Duration: 01:00:07

Speaker: :

Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. As such, it underlies a huge variety of analyses in the neurosciences. This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

Difficulty level: Beginner

Duration: 01:21:30

Speaker: :

This lecture covers describing and characterizing an input-output relationship.

Difficulty level: Beginner

Duration: 1:35:33

Speaker: : Jonathan D. Victor

Part 1 of 2 of a tutorial on statistical models for neural data

Difficulty level: Beginner

Duration: 1:45:48

Speaker: : Jonathan Pillow

Part 2 of 2 of a tutorial on statistical models for neural data.

Difficulty level: Beginner

Duration: 1:50:31

Speaker: : Jonathan Pillow

Course:

Introduction to stability analysis of neural models

Difficulty level: Intermediate

Duration: 1:26:06

Speaker: : Bard Ermentrout

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