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:

Introduction to simple spiking neuron models.

Difficulty level: Beginner

Duration: 48 Slides

Speaker: : Zubin Bhuyan

Introduction to simple spiking neuron models.

Difficulty level: Beginner

Duration: 48 Slides

Speaker: : Zubin Bhuyan

Course:

FAIR principles and methods currently in development for assessing FAIRness.

Difficulty level: Beginner

Duration:

Speaker: : Michel Dumontier

Audio slides presentation to accompany the paper titled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. Authors: M. Schirner, S. Rothmeier, V. Jirsa, A.R. McIntosh, P. Ritter.

Difficulty level: Beginner

Duration: 4:56

Speaker: :

Course:

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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".

This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about. This lecture contains links to 3 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

Difficulty level: Beginner

Duration: 27:48

Speaker: : Gunnar Blohm

Course:

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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".

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 question and answer sessions.

Difficulty level: Beginner

Duration: 29:52

Speaker: : Megan Peters

Course:

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that 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".

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

Course:

This lecture provides an overview of generalized linear models (GLM) and contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

Difficulty level: Beginner

Duration: 33:58

Speaker: : Cristina Savin

Course:

This lecture further develops the concepts introduced in Machine Learning I.

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 provides an 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

Course:

This is part 1 of a 2-part series about 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). In the next tutorial, we’ll extend to a special case of GLMs, logistic regression, and learn how to ensure good model performance. This tutorial is designed to run with retinal ganglion cell spike train data from Uzzell & Chichilnisky 2004.

Difficulty level: Beginner

Duration: 8:09

Speaker: : Anqi Wu

Course:

This is continuation of part 1 of a 2-part series about 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). In the next tutorial, we’ll extend to a special case of GLMs, logistic regression, and learn how to ensure good model performance.

This tutorial is designed to run with retinal ganglion cell spike train data from Uzzell & Chichilnisky 2004.

Difficulty level: Beginner

Duration: 8:02

Speaker: : Anqi Wu

Course:

In this 4 part tutorial, we'll explore how multivariate data can be represented in different orthonormal bases. This will help us build intuition that will be helpful in understanding PCA in the following tutorial.

Overview of tutorial:

- Generate correlated multivariate data
- Define an arbitrary orthonormal basis
- Project the data onto the new basis

Difficulty level: Beginner

Duration: 4:48

Speaker: : Alex Cayco Gajic

Course:

In this 4 part tutorial, we'll explore how multivariate data can be represented in different orthonormal bases. This will help us build intuition that will be helpful in understanding PCA in the following tutorial.

Overview of the tutorial:

- Generate correlated multivariate data
- Define an arbitrary orthonormal basis
- Project the data onto the new basis

Difficulty level: Beginner

Duration: 2:41

Speaker: : Alex Cayco Gajic

Course:

In this 4 part tutorial, we'll explore how multivariate data can be represented in different orthonormal bases. This will help us build intuition that will be helpful in understanding PCA in the following tutorial.

Overview of tutorial:

- Generate correlated multivariate data
- Define an arbitrary orthonormal basis
- Project the data onto the new basis

Difficulty level: Beginner

Duration: 1:56

Speaker: : Alex Cayco Gajic

Course:

In this 4 part tutorial, we'll learn how to perform principal component analysis (PCA) by projecting the data onto the eigenvectors of its covariance matrix.

Overview:

- Calculate the eigenvectors of the sample covariance matrix.
- Perform PCA by projecting data onto the eigenvectors of the covariance matrix.
- Plot and explore the eigenvalues.

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

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