In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Dan Goodman

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

Difficulty level: Intermediate

Duration: 6:54

Speaker: : Marcus Ghosh

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Difficulty level: Beginner

Duration: 37:51

Speaker: : Barbara Sperner-Unterweger

Course:

This lesson gives an introduction to simple spiking neuron models.

Difficulty level: Beginner

Duration: 48 Slides

Speaker: : Zubin Bhuyan

This lesson provides an introduction to simple spiking neuron models.

Difficulty level: Beginner

Duration: 48 Slides

Speaker: : Zubin Bhuyan

This presentation accompanies the paper entitled: *An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data *(see link below to download publication).

Difficulty level: Beginner

Duration: 4:56

Course:

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced

Duration: 50:28

Speaker: : Pierre Bellec

This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.

Difficulty level: Beginner

Duration: 27:48

Speaker: : Gunnar Blohm

Course:

This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded Q&A sessions.

Difficulty level: Beginner

Duration: 29:52

Speaker: : Megan Peters

Course:

This lecture formalizes modeling as a decision process that is constrained by a precise problem statement and specific model goals. We provide real-life examples on how model building is usually less linear than presented in Modeling Practice I.

Difficulty level: Beginner

Duration: 22:51

Speaker: : Gunnar Blohm

Course:

This lecture focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling.

Difficulty level: Beginner

Duration: 26:46

Speaker: : Jan Drugowitsch

Course:

This lecture summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.

Difficulty level: Beginner

Duration: 38.17

Speaker: : Kunlin Wei

This lecture provides an overview of the generalized linear models (GLM) course, originally a part of the Neuromatch Academy (NMA), an interactive online summer school held in 2020. NMA provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".

Difficulty level: Beginner

Duration: 33:58

Speaker: : Cristina Savin

This lecture further develops the concepts introduced in Machine Learning I. This lecture is part of the Neuromatch Academy (NMA), an interactive online computational neuroscience summer school held in 2020.

Difficulty level: Beginner

Duration: 29:30

Speaker: : I. Memming Park

Course:

This lecture introduces the core concepts of dimensionality reduction.

Difficulty level: Beginner

Duration: 31:43

Speaker: : Byron Yu

Course:

This lecture covers the application of dimensionality reduction applied to multi-dimensional neural recordings using brain-computer interfaces with simultaneous spike recordings.

Difficulty level: Beginner

Duration: 30:15

Speaker: : Byron Yu

This is a tutorial covering Generalized Linear Models (GLMs), which are a fundamental framework for supervised learning. In this tutorial, the objective is to model a retinal ganglion cell spike train by fitting a temporal receptive field: first with a Linear-Gaussian GLM (also known as ordinary least-squares regression model) and then with a Poisson GLM (aka "Linear-Nonlinear-Poisson" model). The data you will be using was published by Uzzell & Chichilnisky 2004.

Difficulty level: Beginner

Duration: 8:09

Speaker: : Anqi Wu

Course:

This tutorial covers multivariate data can be represented in different orthonormal bases.

Difficulty level: Beginner

Duration: 4:48

Speaker: : Alex Cayco Gajic

Course:

This tutorial covers how to perform principal component analysis (PCA) by projecting the data onto the eigenvectors of its covariance matrix.

To quickly refresh your knowledge of eigenvalues and eigenvectors, you can watch this short video (4 minutes) for a geometrical explanation. For a deeper understanding, this in-depth video (17 minutes) provides an excellent basis and is beautifully illustrated.

Difficulty level: Beginner

Duration: 6:33

Speaker: : Alex Cayco Gajic

Course:

This tutorial covers how to apply principal component analysis (PCA) for dimensionality reduction, using a classic dataset that is often used to benchmark machine learning algorithms: MNIST. We'll also learn how to use PCA for reconstruction and denoising.

You can learn more about MNIST dataset here.

Difficulty level: Beginner

Duration: 5:35

Speaker: : Alex Cayco Gajic

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