2020 Schedule | Neurohack Years | Neurohackademy
https://neurohackademy.org/neurohack_year/2020/
لینک یوتیوب کورس ها بعد از برگزاری هر جلسه به برنامه زمانی اضافه می شود.

neurohackademy/nh2020-curriculum: Materials for NH2020
https://github.com/neurohackademy/nh2020-curriculum
آدرس گیتهاب Neurohackademy برای نوت بوک های جلسات.

C++ implementation of the Python Numpy library
GITHUB
DOCUMENTATION

برای استفاده از کتابخانه numcpp باید کتابخانه boost نصب شده و آپدیت باشد. برای آپدیت کردن کتابخانه boost به اینجا و اینجا نگاه کنید.
برای راهنمای سریع استفاده از کتابخانه به صفحه اول README.md گیتهاب مراجعه کنید.

موج دوم بیماری کرونا در کشورهای مختلف به روایت آمار.
گیتهاب برای دسترسی به داده ها و کد.
data reference : WHO

Github
مدرسه تابستانی neuromatchacademy به پایان رسید. منابع و ویدئوها در دسترس عموم اند و منبع خوبی برای آشنایی با طیف وسیعی از مطالب تحلیل داده، نوروساینس و ... است.

🔆 STAN
☘️ Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation.
☘️ Stan interfaces with the most popular data analysis languages (R, #python, shell, MATLAB, Julia, Stata) and runs on all major platforms (Linux, Mac, Windows).

🌱 Stan User’s Guide
🌱 PyStan Guide
To install simply use:
$ pip3 install pystan

#CCS2020 Conference on Complex Systems 2020, online 7-12 December.
LINK

Bernstein Network Computational Neuroscience

We are extremely happy to let you know that the registration for the #BernsteinConference 2020 is now open! This year, the conference is free of charge but registration is mandatory.
Link

ALLEN INSTITUTE MODELING WORKSHOP
The workshop will run from Wednesday, August 12 to Friday, August 14, and begin at 8am Pacific Time each day (7:30 PM in Tehran Time)

Spiking neural networks as universal function approximators: Learning algorithms and applications
(Aug 31st / Sep 1st 2020)

The last years have seen many exciting new developments to train spiking neural networks to perform complex information processing. This online workshop brings together researchers in the field to present their work and discuss ways of translating these findings into a better understanding of neural circuits. Topics include artificial and biologically plausible learning algorithms and the dissection of trained spiking circuits toward understanding neural processing. We have a manageable number of talks with ample time for discussions.

The workshop is being organised by Dan Goodman and Friedemann Zenke.
Register here

#workshop

I am working on Modeling neural dynamics from Borgers using #brian2.

It's easy to learn and fast for development.
for a quick review and tutorial look at here.
There is also a course by #Gerstner which use brain2 in his book. [link to exercise]
The course is available by EPFL.
I am uploading the jupyter note books of Borgers using brian2 here.
Have fun coding!

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