In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.

Difficulty level: Intermediate

Duration: 29:56

Speaker: : Aurel A. Lazar

Course:

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and go through both motivation and process involved in moving your research computing to the cloud. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate

Duration: 3:09:12

Speaker: : Amanda Tan

This lecture on multi-scale entropy by Jil Meier is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate

Duration: 39:05

Speaker: : Jil Meier

This lecture on generating TVB ready imaging data by Paul Triebkorn is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate

Duration: 1:40:52

Speaker: : Paul Triebkorn

This lecture on modeling epilepsy using TVB by Julie Courtiol is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate

Duration: 37:12

Speaker: : Julie Courtiol

This lecture 1/15 is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 0:40

Speaker: : Florence I. Kleberg

This lecture (2/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:23

Speaker: : Florence I. Kleberg

This lecture (3/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:20

Speaker: : Florence I. Kleberg

This lecture (4/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:08

Speaker: : Florence I. Kleberg

This lecture (5/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:18

Speaker: : Florence I. Kleberg

This lecture (6/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures. Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:26

Speaker: : Florence I. Kleberg

This lecture (7/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 0:42

Speaker: : Florence I. Kleberg

This lecture (8/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 2:40

Speaker: : Florence I. Kleberg

This lecture (9/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 2:54

Speaker: : Florence I. Kleberg

This lecture (10/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:43

Speaker: : Florence I. Kleberg

This lecture (11/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 2:58

Speaker: : Florence I. Kleberg

This lecture (12/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 2:08

Speaker: : Florence I. Kleberg

This lecture (13/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures. Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:58

Speaker: : Florence I. Kleberg

This lecture (14/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:40

Speaker: : Florence I. Kleberg

This lecture (15/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 0:37

Speaker: : Florence I. Kleberg

- Developmental neuroscience (3)
- Brain computer interface (1)
- Standards and Best Practices (2)
- Digital brain atlasing (4)
- Epilepsy (1)
- Neuroimaging (5)
- Brain networks (1)
- Neuromorphic engineering (1)
- Electrophysiology (17)
- Standards and best practices (11)
- Neuroanatomy (4)
- Tools (11)
- (-) Neuronal plasticity (15)
- Data governance (2)
- Data sharing (1)
- Data standard (1)
- (-) Clinical neuroinformatics (2)
- Computational neuroscience (64)
- Computer Science (1)
- Data science (2)
- Open science (2)
- Education (1)
- Neuroethics (2)