Course:

This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

Difficulty level: Intermediate

Duration: 17:54

Speaker: : Mike X. Cohen

Course:

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.

Difficulty level: Intermediate

Duration: 5:02

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.

Difficulty level: Intermediate

Duration: 15:01

Speaker: : Mike X. Cohen

Course:

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

Difficulty level: Intermediate

Duration: 5:15

Speaker: : Mike X. Cohen

Course:

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

Difficulty level: Intermediate

Duration: 22:41

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.

Difficulty level: Intermediate

Duration: 17:19

Speaker: : Mike X. Cohen

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

Course:

This tutorial provides instruction on how to interact with and leverage Python packages to get the most out of Python's suite of available tools for the manipulation, management, analysis, and visualization of neuroscientific data.

Difficulty level: Intermediate

Duration: 1:26:02

Speaker: : Ariel Rokem

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.

Difficulty level: Intermediate

Duration: 5:58

Speaker: : Dan Goodman

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:51

Speaker: : Dan Goodman

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, *Understanding Neural Networks*.

Difficulty level: Intermediate

Duration: 2:43

Speaker: : Marcus Ghosh

Course:

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package.

Difficulty level: Intermediate

Duration: 2:22:28

Speaker: : Jake Vanderplas

Course:

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Ari Ercole

This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.

Difficulty level: Intermediate

Duration: 39:05

Speaker: : Jil Meier

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

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