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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

How genetics can contribute to our understanding of psychiatric phenotypes.

Difficulty level: Beginner
Duration: 55:15
Speaker: : Sven Cichon

An overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the Central Nervous System.

Difficulty level: Beginner
Duration: 45:47

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

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

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

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

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 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

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 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 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 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

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 further develops the concepts introduced in Machine Learning I.

Difficulty level: Beginner
Duration: 29:30
Speaker: : I. Memming Park

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 introduces the core concepts of dimensionality reduction.

Difficulty level: Beginner
Duration: 31:43
Speaker: : Byron Yu

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 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

This tutorial covers 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). This tutorial also covers 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

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

 

    Overview of this 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

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

    Overview of this tutorial:

    • 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

    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.

    Overview of this tutorial:

    • Perform PCA on MNIST
    • Calculate the variance explained
    • Reconstruct data with different numbers of PCs
    • (Bonus) Examine denoising using PCA

    You can learn more about MNIST dataset here.

    Difficulty level: Beginner
    Duration: 5:35
    Speaker: : Alex Cayco Gajic