Course:

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate

Duration: 5:17

Speaker: : Mike X. Cohen

Course:

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate

Duration: 11:37

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram.

Difficulty level: Intermediate

Duration: 5:31

Speaker: : Mike X. Cohen

Course:

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons.

Difficulty level: Intermediate

Duration: 13:48

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

In this lesson, users are shown how to create a spatial map of neuronal orientation tuning.

Difficulty level: Intermediate

Duration: 13:11

Speaker: : Mike X. Cohen

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

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 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

This tutorial covers the fundamentals of collaborating with Git and GitHub.

Difficulty level: Intermediate

Duration: 2:15:50

Speaker: : Elizabeth DuPre

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

This lecture covers the concept of inference in 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: 1:01:04

Speaker: : Alfredo Canziani

- Electroencephalography (EEG) (10)
- (-) Deep learning (10)
- Bayesian networks (2)
- Clinical neuroinformatics (2)
- Standards and Best Practices (1)
- Neuroimaging (18)
- Tools (1)
- Clinical neuroscience (1)
- (-) General neuroscience (6)
- Computational neuroscience (5)
- Statistics (3)
- (-) Computer Science (1)
- Genomics (8)
- Data science (2)
- (-) Open science (4)