http://neuronschool.uniss.it/ Principles of Computational Neuroscience School

♦ https://www.nature.com/articles/d41586-018-01690-2

🌴 In a new paper, SFI External Prof Jon Machta and colleagues from UC Davis, show that one of the most famous models in statistical physics — the #Ising model — could be used to understand why pistachio trees bloom in synchrony.

https://santafe.edu/news-center/news/what-magnets-have-do-pistachios

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹شنبه، ۲۱ بهمن‌، ساعت ۱۵:۱۵ - کلاس ۲ دانشکده فیزیک دانشگاه شهید بهشتی

@carimi

✅ https://www.sciencenews.org/blog/scicurious/wikipedia-science-reference-citations

📐 https://ideas.ted.com/gallery-how-networks-help-us-understand-the-world/

Introduction to the strategy and methods of complex systems Professor Yaneer Bar-Yam, New England Complex Systems Institute

https://www.elgaronline.com/view/9781785364419.00009.xml?pdfVersion=true

🔸 A very physics view of deep learning networks: Schwartz-Ziv & Tishby "Opening the black box of Deep Neural Networks via Information" layered neural networks as a Markov chain:

🔗 https://arxiv.org/pdf/1703.00810

🔖 From the difference of structures to the structure of the difference

Massimiliano Zanin, Ernestina Menasalvas, Xiaoqian Sun, Sebastian Wandelt

🔗 arxiv.org/pdf/1802.03966.pdf

📌 ABSTRACT
When dealing with evolving or multi-dimensional complex systems, network theory provides with elegant ways of describing their constituting components, through respectively time-varying and multi-layer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. As opposed to other approaches, as for instance the use of global overlap or entropies, the proposed one allows to understand if the observed changes are due to random noise, or to structural (targeted) modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow to convert the system's evolution to the frequency domain.

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹شنبه، ۲۸ بهمن‌، ساعت ۱۵:۱۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی

@carimi

💭 http://advances.sciencemag.org/content/4/2/e1700578

‏۳۰ سال پیش #فاینمن به خاطر #سرطان فوت کرد. یکی از تاثیرگذارترین چهره‌هایی که تاریخ علم به خودش دیده!

http://www.sitpor.org/2017/11/heros-feynman/

🔸 https://www.quantamagazine.org/scant-evidence-of-power-laws-found-in-real-world-networks-20180215/

🔹اولین کارگاه مبانی پردازش سیگنالهای حیاتی با نرم افزار متلب با محوریت علوم اعصاب شناختی

ثبت نام و کسب اطلاعات بیشتر:
www.nbml.ir/fa/workshops


@eegworkshop

The Influence of
Benoît B. Mandelbrot
on Mathematics

Edited by Michael F. Barnsley and Michael Frame

Lectures from the 2017 MIND Summer School
MIND Summer School
Watch the lectures from the 2017 Methods in Neuroscience at Dartmouth Summer School (MIND). More details about the speakers and the schedule can be seen on our website https://summer-mind.github.io/

🎞https://youtube.com/playlist?list=PLEE6ggCEJ0H3EiFQeINxfJq-kqlrELf66

🎯 Percolation theory: play with it and learn about it with interactive Complexity Explorables:
http://rocs.hu-berlin.de/explorables/explorables/0.592746/