This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.

Difficulty level: Intermediate

Duration: 1:29:08

Speaker: : Shreejoy Tripathy

This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat.

Difficulty level: Intermediate

Duration: 1:19:17

Speaker: : Sonny Chen

This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD).

Difficulty level: Intermediate

Duration: 1:15:14

Speaker: : Keon Arbabi

This is a tutorial on how to simulate neuronal spiking in brain microcircuit models, as well as how to analyze, plot, and visualize the corresponding data.

Difficulty level: Intermediate

Duration: 1:39:50

Speaker: : Frank Mazza

Course:

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.

Difficulty level: Intermediate

Duration: 1:39:04

Speaker: : Erin Dickie and John Griffiths

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.

This lesson corresponds to slides 1-64 in the PDF below.

Difficulty level: Intermediate

Duration: 1:28:14

Speaker: : Andreea Diaconescu

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

Difficulty level: Intermediate

Duration: 1:21:38

Speaker: : Dan Felsky

This lesson provides an overview of the CaImAn package, as well as a demonstration of usage with NWB.

Difficulty level: Intermediate

Duration: 44:37

Speaker: : Andrea Giovannucci

This lesson gives an overview of the SpikeInterface package, including demonstration of data loading, preprocessing, spike sorting, and comparison of spike sorters.

Difficulty level: Intermediate

Duration: 1:10:28

Speaker: : Alessio Buccino

In this lesson, users will learn about the NWBWidgets package, including coverage of different data types, and information for building custom widgets within this framework.

Difficulty level: Intermediate

Duration: 47:15

Speaker: : Ben Dichter

This is the Introductory Module to the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Difficulty level: Intermediate

Duration: 50:17

Speaker: : Yann LeCun and Alfredo Canziani

This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:51:03

Speaker: : Yann LeCun

This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:59:47

Speaker: : Yann LeCun and Alfredo Canziani

This lecture covers the concept of convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 51:40

Speaker: : Yann LeCun

This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:09:12

Speaker: : Alfredo Canziani

This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:05:36

Speaker: : Alfredo Canziani

This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy-based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:51:30

Speaker: : Yann LeCun

- (-) Deep learning (10)
- Bayesian networks (2)
- Clinical neuroinformatics (2)
- Standards and Best Practices (1)
- Neuroimaging (16)
- Standards and best practices (3)
- Tools (1)
- Clinical neuroscience (1)
- General neuroscience (6)
- Computational neuroscience (20)
- Statistics (3)
- Computer Science (1)
- Genomics (8)
- Data science (2)
- Open science (4)