The Virtual Brain
TheVirtualBrain is a framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity.
The TVB EduPack on the TVB main website contains dozens of video lectures explaining TVB’s GUI and scripting interfaces. Some lectures help you reproduce the research done for a peer-reviewed neuroscience publication:
Tutorials.
Basics: My first simulation with The Virtual Brain, lecture by Paul Triebkorn
Basics: Population Models in The Virtual Brain (TVB) and the Phase Plane, lecture by Dr. Michael Schirner
Epilepsy: Modelling Epilepsy with The Virtual Brain, lecture by Julie Courtiol
Epilepsy: The Bayesian Virtual Epileptic Patient (BVEP), lecture by Meysam Hashemi
Stimulation: Brain stimulation in The Virtual Brain, lecture by Andreas Spiegler
Stimulation: Surface-based simulations and deep brain stimulations, lecture by Jil Meier
Stroke: TVB Clinical Applications - Stroke Recovery & Dementia, lecture by Randy McIntosh
Stroke: Hands-On: Modeling stroke brain, lecture by Paul Triebkorn
Model construction: Import Virtual Brain ready data into TVB and create a brain model, lecture by Patrik Bey
Model construction: Integrated workflows: Image processing pipeline, lecture by Dr. Michael Schirner
Special applications: Modeling brain dynamics in brain tumor patients using The Virtual Brain
Special applications: Simulating The Virtual Mouse Brain (TVMB), lecture by Patrik Bey
Advanced simulation: Human Brain Project (HBP) TVB-NEST co-simulation
Advanced simulation: TVB to NEST multi-scale simulation, lecture by Dionysios Perdikis
Other links are Here.
#TVB
TheVirtualBrain is a framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity.
The TVB EduPack on the TVB main website contains dozens of video lectures explaining TVB’s GUI and scripting interfaces. Some lectures help you reproduce the research done for a peer-reviewed neuroscience publication:
Tutorials.
Basics: My first simulation with The Virtual Brain, lecture by Paul Triebkorn
Basics: Population Models in The Virtual Brain (TVB) and the Phase Plane, lecture by Dr. Michael Schirner
Epilepsy: Modelling Epilepsy with The Virtual Brain, lecture by Julie Courtiol
Epilepsy: The Bayesian Virtual Epileptic Patient (BVEP), lecture by Meysam Hashemi
Stimulation: Brain stimulation in The Virtual Brain, lecture by Andreas Spiegler
Stimulation: Surface-based simulations and deep brain stimulations, lecture by Jil Meier
Stroke: TVB Clinical Applications - Stroke Recovery & Dementia, lecture by Randy McIntosh
Stroke: Hands-On: Modeling stroke brain, lecture by Paul Triebkorn
Model construction: Import Virtual Brain ready data into TVB and create a brain model, lecture by Patrik Bey
Model construction: Integrated workflows: Image processing pipeline, lecture by Dr. Michael Schirner
Special applications: Modeling brain dynamics in brain tumor patients using The Virtual Brain
Special applications: Simulating The Virtual Mouse Brain (TVMB), lecture by Patrik Bey
Advanced simulation: Human Brain Project (HBP) TVB-NEST co-simulation
Advanced simulation: TVB to NEST multi-scale simulation, lecture by Dionysios Perdikis
Other links are Here.
#TVB
YouTube
TVB Node 10: My first simulation with The Virtual Brain by Paul Triebkorn
This presentation 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.…
Have you ever wondered how models of resting state fMRI really perform? Then this is the thread.
GitHub
GitHub
GitHub
GitHub - KevinAquino/modelling_comparisons: A series of scripts and tools to model large scale biophysical models for fMRI.
A series of scripts and tools to model large scale biophysical models for fMRI. - GitHub - KevinAquino/modelling_comparisons: A series of scripts and tools to model large scale biophysical models f...
Human connectome courses
The Human Connectome Project offers intensive training courses to the neuroscience community in the acquisition, analysis and visualization of data using methods and informatics tools developed by the WU-Minn HCP consortium.
HCP Course : Exploring the Human Connectome
2015-2019 fully available.
The Human Connectome Project offers intensive training courses to the neuroscience community in the acquisition, analysis and visualization of data using methods and informatics tools developed by the WU-Minn HCP consortium.
HCP Course : Exploring the Human Connectome
2015-2019 fully available.
timeTable.pdf
431.5 KB
26th Annual IASBS Meeting on Condensed Matter Physics – July 7, 2021-July 9, 2021
Semir Zeki and Bard Ermentrout are among the speakers.
https://iasbs.ac.ir/~condmat-meeting/m26/
توضیحات فارسی
Semir Zeki and Bard Ermentrout are among the speakers.
https://iasbs.ac.ir/~condmat-meeting/m26/
توضیحات فارسی
My first post on #Medium on solving ill-conditioned system of equations using Multi-precision computations. 🙃
Link
Link
Medium
Numerical solving system of equations (ill-conditioned)
How to solve a system of equations Ax=b when the coefficient matrix is ill-conditioned? Such a matrix is almost singular, and the…
Using #ffmpeg to convert video formats, fast and without sensible loosing quality:
Converted video has only 1/3 of original file size.
🪟 Windows users:
1. Download 'ffmpeg'
https://www.gyan.dev/ffmpeg/builds/ffmpeg-git-full.7z
2. unzip file in somewhere like : C:\Program Files\ffmpeg
now you have folders like bin, doc , .. in this directory.
3. add path, so windows know where is it
Properties > Advanced System Settings > Advanced tab > Environment Variables
In the Environment Variables window, click the "Path" row under the "Variable" column, then click Edit > click NEW
paste this address:
C:\Program Files\ffmpeg\bin
Done!
😎 Debian users:
$ sudo apt install ffmpeg
Done!
now you open a windows shell and convert the video:
go to the directory that movie file exist:
for example :
cd .\Downloads\
ffmpeg -i input.webm -r 10 -cpu-used 5 -c:v libx264 -crf 20 -c:a aac -strict experimental -loglevel error output.mp4
20+ FFmpeg Commands For Beginners
A quick guide for using ffmpeg
Converted video has only 1/3 of original file size.
🪟 Windows users:
1. Download 'ffmpeg'
https://www.gyan.dev/ffmpeg/builds/ffmpeg-git-full.7z
2. unzip file in somewhere like : C:\Program Files\ffmpeg
now you have folders like bin, doc , .. in this directory.
3. add path, so windows know where is it
Properties > Advanced System Settings > Advanced tab > Environment Variables
In the Environment Variables window, click the "Path" row under the "Variable" column, then click Edit > click NEW
paste this address:
C:\Program Files\ffmpeg\bin
Done!
😎 Debian users:
$ sudo apt install ffmpeg
Done!
now you open a windows shell and convert the video:
go to the directory that movie file exist:
for example :
cd .\Downloads\
ffmpeg -i input.webm -r 10 -cpu-used 5 -c:v libx264 -crf 20 -c:a aac -strict experimental -loglevel error output.mp4
20+ FFmpeg Commands For Beginners
A quick guide for using ffmpeg
OSTechNix
20+ FFmpeg Commands For Beginners - OSTechNix
This guide lists the most commonly and frequently used 20+ ffmpeg commands. These commands are just enough to getting started with FFmpeg.
Where to start machine learning?
Here is a complete guide by Santiago
https://twitter.com/svpino/status/1407700129562435586
Here is a complete guide by Santiago
https://twitter.com/svpino/status/1407700129562435586
If you can't afford to pay the $5, reply to this tweet, and I'll share with you a free copy of the course.
So far, 339 people have taken advantage of this!
https://twitter.com/svpino/status/1407750918121181191
So far, 339 people have taken advantage of this!
https://twitter.com/svpino/status/1407750918121181191
Twitter
Santiago
If you can't afford to pay the $5, reply to this tweet, and I'll share with you a free copy of the course. So far, 339 people have taken advantage of this!
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Look at Float32 vs Float64: O(1) difference => O(1) derivative error.
LINK
Shadowing Methods for Forward and Adjoint Sensitivity Analysis of Chaotic Systems
+julia codes
#julia
LINK
Shadowing Methods for Forward and Adjoint Sensitivity Analysis of Chaotic Systems
+julia codes
#julia
Online lecture series "Neural Data Science" on how to use #MachineLearning for #neuroscience is now complete
YouTube
GitHub
YouTube
GitHub
YouTube
Neural Data Science — Philipp Berens, 2021
Share your videos with friends, family, and the world
UVA DEEP LEARNING COURSE
MSc in Artificial Intelligence for the University of Amsterdam.
https://uvadlc.github.io/
GitHub
#course
#DL
MSc in Artificial Intelligence for the University of Amsterdam.
https://uvadlc.github.io/
GitHub
#course
#DL
GitHub
uvadlc_notebooks/docs/tutorial_notebooks at master · phlippe/uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023 - phlippe/uvadlc_notebooks
scikit-learn course
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.
The course description can be found here.
GitHub
#course
#MachineLearning
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.
The course description can be found here.
GitHub
#course
#MachineLearning
GitHub
GitHub - INRIA/scikit-learn-mooc: Machine learning in Python with scikit-learn MOOC
Machine learning in Python with scikit-learn MOOC. Contribute to INRIA/scikit-learn-mooc development by creating an account on GitHub.