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
Course:
This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.
Difficulty level: Intermediate
Duration: 8:21
Speaker: : Mike X. Cohen
Course:
This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.
Difficulty level: Intermediate
Duration: 22:01
Speaker: : Mike X. Cohen
Course:
This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.
Difficulty level: Intermediate
Duration: 11:20
Speaker: : Mike X. Cohen
Course:
This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.
Difficulty level: Intermediate
Duration: 20:39
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: :