Course:

Next generation science with Jupyter. This lecture was part of the 2019 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: 50:28

Speaker: : Elizabeth DuPre

Learn how to create a standard extracellular electrophysiology dataset in NWB using Python

Difficulty level: Intermediate

Duration: 45:46

Speaker: : Ryan Ly

Learn how to create a standard calcium imaging dataset in NWB using Python

Difficulty level: Intermediate

Duration: 31:04

Speaker: : Ryan Ly

Learn how to create a standard intracellular electrophysiology dataset in NWB

Difficulty level: Intermediate

Duration: 20:23

Speaker: : Pamela Baker

Learn how to use the icephys-metadata extension to enter meta-data detailing your experimental paradigm

Difficulty level: Intermediate

Duration: 27:18

Speaker: : Oliver Ruebel

Learn how to create a standard extracellular electrophysiology dataset in NWB using MATLAB

Difficulty level: Intermediate

Duration: 45:46

Speaker: : Ben Dichter

Learn how to create a standard calcium imaging dataset in NWB using MATLAB

Difficulty level: Intermediate

Duration: 39:10

Speaker: : Ben Dichter

Learn how to create a standard intracellular electrophysiology dataset in NWB

Difficulty level: Intermediate

Duration: 20:22

Speaker: : Pamela Baker

Overview of the Braintorm package for analyzing extracellular electrophysiology, including preprocessing, spike sorting, trial alignment, and spectrotemporal decomposition

Difficulty level: Intermediate

Duration: 47:47

Speaker: : Konstantinos Nasiotis

Overview of the CaImAn package, and demonstration of usage with NWB

Difficulty level: Intermediate

Duration: 44:37

Speaker: : Andrea Giovannucci

Overview of the SpikeInterface package, including demonstration of data loading, preprocessing, spike sorting, and comparison of spike sorters

Difficulty level: Intermediate

Duration: 1:10:28

Speaker: : Alessio Buccino

Overview of the NWBWidgets package, including coverage of different data types, and information for building custom widgets within this framework

Difficulty level: Intermediate

Duration: 47:15

Speaker: : Ben Dichter

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

- Standards and Best Practices (2)
- (-) Neuronal plasticity (15)
- Clinical neuroinformatics (2)
- Electroencephalography (EEG) (1)
- Magnetoencephalography (MEG) (1)
- Connectivity (1)
- Brain networks (1)
- Cloud computing (1)
- Neuroimaging (4)
- Computational neuroscience (31)
- (-) Electrophysiology (11)
- Standards and best practices (11)
- (-) Data science (1)
- Tools (6)
- (-) Open science (1)