Course:

This lesson teaches users how to generate a frequency-current (F-I) curve, which describes the function that relates the net synaptic current (I) flowing into a neuron to its firing rate (F).

Difficulty level: Intermediate

Duration: 20:39

Speaker: : Mike X. Cohen

Course:

This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.

Difficulty level: Intermediate

Duration: 7:15

Speaker: : Mike X. Cohen

Course:

This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.

Difficulty level: Intermediate

Duration: 12:15

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.

Difficulty level: Intermediate

Duration: 12:05

Speaker: : Mike X. Cohen

Course:

In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.

Difficulty level: Intermediate

Duration: 20:12

Speaker: : Mike X. Cohen

Course:

This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.

Difficulty level: Intermediate

Duration: 12:52

Speaker: : Mike X. Cohen

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:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 5:02

Speaker: : Mike X. Cohen

Course:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 15:01

Speaker: : Mike X. Cohen

Course:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 5:15

Speaker: : Mike X. Cohen

Course:

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Mike X. Cohen

Course:

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Mike X. Cohen

Course:

Difficulty level: Intermediate

Duration: 22:41

Speaker: : Mike X. Cohen

Course:

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

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

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

- Deep learning (25)
- Electroencephalography (EEG) (10)
- Bayesian networks (3)
- Clinical neuroinformatics (2)
- Standards and Best Practices (1)
- Neuroimaging (20)
- Tools (1)
- Clinical neuroscience (1)
- (-) General neuroscience (6)
- Computational neuroscience (5)
- Statistics (3)
- Computer Science (1)
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- Data science (2)
- Open science (4)