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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

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

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

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

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

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

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

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

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

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

This lecture provides an introduction to the study of eye-tracking in humans. 

Difficulty level: Beginner
Duration: 34:05
Speaker: : Ulrich Ettinger

This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

Difficulty level: Beginner
Duration: 37:47

Hardware for computing for non-ICT specialists

Difficulty level: Beginner
Duration: 43:21
Speaker: : Steve Furber

This lecture provides a history of data management, recent developments data management, and a brief description of scientific data management.

Difficulty level: Advanced
Duration: 35:10
Speaker: : Thomas Heinis

Computer arithmetic is necessarily performed using approximations to the real numbers they are intended to represent, and consequently it is possible for the discrepancies between the actual solution and the approximate solutions to diverge, i.e. to become increasingly different. This lecture focuses on how this happens and techniques for reducing the effects of these phenomena and discuss systems which are chaotic.

Difficulty level: Beginner
Duration: 36:56
Speaker: : David Lester

This lecture will addresses what it means for a problem to have a computable solution, methods for combining computability results to analyse more complicated problems, and finally look in detail at one particular problem which has no computable solution: the halting problem.

Difficulty level: Beginner
Duration: 28:28
Speaker: : David Lester

This lecture focuses on computational complexity which lies at the heart of computer science thinking. In short, it is a way to quickly gauge an approximation to the computational resource required to perform a task. Methods to analyse a computer program and to perform the approximation are presented. Speaker: David Lester.

Difficulty level: Beginner
Duration: 27:33
Speaker: : David Lester

This lecture gives an introduction to simulation, models, and the neural simulation tool NEST. 

Difficulty level: Beginner
Duration: 1:48:18

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

Difficulty level: Beginner
Duration: 1:23:01
Speaker: : Gaute Einevoll

This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies. 

Difficulty level: Beginner
Duration: 1:30:45
Speaker: : Maryann Martone