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 Types I and further explains how models can be used answer different scientific questions.

Difficulty level: Beginner

Duration: 32:30

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 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

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 further develops the concepts introduced in Machine Learning I.

Difficulty level: Beginner

Duration: 29:30

Speaker: : I. Memming Park

An overview of the process of developing the TVB-NEST co-simulation on the EBRAINS infrastructure, and it's use cases.

Difficulty level: Beginner

Duration: 0:25:14

Speaker: : Denis Perdikis

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 Tutorial 1 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

Difficulty level: Beginner

Duration: 6:18

Speaker: : Anqi Wu

Course:

This is Tutorial 2 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will use a different approach to fit linear models that incorporates the random 'noise' in our data.

Difficulty level: Beginner

Duration: 8:00

Speaker: : Anqi Wu

Course:

This is Tutorial 3 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will discuss how to gauge how good our estimated model parameters are.

Difficulty level: Beginner

Duration: 5:00

Speaker: : Anqi Wu

Course:

This is Tutorial 4 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will generalize the regression model to incorporate multiple features.

Difficulty level: Beginner

Duration: 7:50

Speaker: : Anqi Wu

Course:

This is Tutorial 5 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

In this tutorial, we will learn about the bias-variance tradeoff and see it in action using polynomial regression models.

Difficulty level: Beginner

Duration: 6:38

Speaker: : Anqi Wu

Course:

This is Tutorial 6 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).

Difficulty level: Beginner

Duration: 5:28

Speaker: : Anqi Wu

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 the implementation of logistic regression, a special case of GLMs used to model binary outcomes. Oftentimes the variable you would like to predict takes only one of two possible values. Left or right? Awake or asleep? Car or bus? In this tutorial, we will decode a mouse's left/right decisions from spike train data.

Objectives of this tutorial:

- Learn about logistic regression, how it is derived within the GLM theory, and how it is implemented in scikit-learn
- Apply logistic regression to decode choices from neural responses
- Learn about regularization, including the different approaches and the influence of hyperparameters

Difficulty level: Beginner

Duration: 6:42

Speaker: : Anqi Wu

Course:

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

Course:

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

- Artificial Intelligence (1)
- Notebooks (1)
- protein-protein interactions (1)
- Extracellular signaling (1)
- Animal models (1)
- Assembly 2021 (27)
- Brain-hardware interfaces (12)
- Clinical neuroscience (1)
- International Brain Initiative (2)
- Repositories and science gateways (5)
- Resources (4)
- General neuroscience
(4)
- (-) General neuroinformatics (1)
- (-) Computational neuroscience (63)
- Computer Science (4)
- (-) Genomics (1)
- Data science (5)
- (-) Open science (10)
- Project management (6)
- Education (1)
- Neuroethics (22)