Videos are available here.

#brian2 simulator, introductory tutorial by Dan Goodman.

Watch "Introduction to Brian" on YouTube
https://youtu.be/cAF3UWTzX9A

Watch Petra Ritter present @thevirtualbrain
and @TVB_cloud at the 4th workshop of #CNS2020: Tools and resources for developing and sharing models in #computational #neuroscience
https://www.youtube.com/watch?v=zkl-YbALlP4&feature=youtu.be

Deepnote - Data science notebook for teams
https://deepnote.com/

Brian workshop, 9 Sep, 16-19 Iran time.
The material will be uploaded here.
To install the required package look here.

Please fill the google form for the registration (free of charge).
(Register)
The language of workshop will be in Farsi (Excuseme nonpersian members).
(Add to google calendar)

How to install and run Brian and Jupyter with Anaconda

To install using pip:
$ pip3 install brian2 jupyterlab

🍀 Sept 2020 / Python for Scientist

This is a medium-advanced course in Python tools such as NumPy, SciPy, Matplotlib, and Pandas. It is suitable for people who have a basic understanding of basic Python and want to know some internals and important libraries for science. 

https://scicomp.aalto.fi/training/scip/python-for-scicomp/

🔆 Scientific Programming with Python and Software Engineering Best Practices

🌱 Advanced Numpy
🌱 Introduction to predictive analytics with Pandas and Scikit Learn
🌱 Testing, debugging, profiling
🌱 Packaging and continuous integration

https://telecom-python.telenczuk.pl/materials

🍀 The Algorithms - Python


A beautiful and well-maintained repository of some classic and well-known algorithms from many different domains. This resource can help you understand how to implement algorithms while practicing programming using Python.

https://github.com/TheAlgorithms/Python
@omarsar0

Information theory and self-organisation -- a course on theory and empiricial analysis using the JIDT software

This playlist presents video lectures from a course on using information theory for analysing complex systems, with particular focuses on:
1. Measures of information dynamics: how information is processed in complex systems, including measures of information storage and transfer;
2. Empirical data analysis using the JIDT open-source software - https://github.com/jlizier/jidt

Full course overview, slides and activities will be available from the JIDT wiki at
https://github.com/jlizier/jidt/wiki/Course

A curated list of awesome machine learning frameworks, libraries and software (by language)
https://github.com/josephmisiti/awesome-machine-learning

Firing Rate estimation using ITNG_TOOLBOX
https://github.com/ITNG/itng_toolbox
#itng_toolbox

https://braincriticality.org/
Brain Criticality Virtual Conference 2020 6-8 Oct

Every data scientist should know
#mysql by Mosh

To get the files and databases for this course look at here

Youtube

In a scientific project, there is always a high chance of encountering logical errors that are emerging from dimensions and units. These types of errors are very hard to detect. Hopefully, C++ and other strongly-typed languages help us to avoid such logical errors. One way is to attach units to values to avoid meaningless computation and detect unit-related errors.

For example, it is meaningless to pass a dimensional quantity to a cosine or exponential function. In the same way, it does not make sense to assign a mass to a quantity with a velocity dimension. The STUDIS library is designed to catch such errors at compile-time.

Here is the link to the library:
https://github.com/DiscreteLogarithm/studis
Link to post

First whole mouse brain imaged on new hybrid open-top light-sheet system! Thanks to @huzhao4
Link