Lesson type
Difficulty level
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 5:17
Speaker: : Mike X. Cohen
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 11:37
Speaker: : Mike X. Cohen
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 5:31
Speaker: : Mike X. Cohen
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 13:48
Speaker: : Mike X. Cohen
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 12:16
Speaker: : Mike X. Cohen
Course:
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.
Difficulty level: Intermediate
Duration: 13:11
Speaker: : Mike X. Cohen
Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls. Authors: Hannelore Aerts, Michael Schirner, Ben Jeurissen, DIrk Van Roost, Eric Achten, Petra Ritter, Daniele Marinazzo
Difficulty level: Intermediate
Duration: 10:01
Speaker: :