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A New Spin on the Quantum Brain

A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.

🎯 https://www.quantamagazine.org/20161102-quantum-neuroscience/

#Neuroscience #Quantum_Mechanics #Biology #Chemistry #Nuclear_Physics

#سلسله_سمینارهای_هفتگی گروه سیستم های پیچیده شهید بهشتی
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl

کلیدواژه ها:
#مدل_تراوش ، #استقامت_شبکه ، #دینامیک_شبکه ، #کلاس_جهان_شمولی

اعلام برنامه های سلسله سمینارهای بیوفیزیک پژوهشکده IBB دانشگاه تهران:

📽 http://www.indiana.edu/~video/stream/launchflash.html?folder=video&filename=Network_Sci_Talk_20161024.mp4

🔵 A great review of #networkscience and neurology by @AmarDhand

http://www.nature.com/nrneurol/journal/v12/n10/full/nrneurol.2016.119.html

🔵 🎓 Data Science MOOCs starting this week!

http://us8.campaign-archive1.com/?u=c4e45e47176fffe244e832ba8&id=14066ba5e9

📝 Sensitive dependence of network dynamics on network structure

Takashi Nishikawa, Jie Sun, Adilson E. Motter

https://arxiv.org/pdf/1611.01164v1

📌 ABSTRACT
The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

#Adaptation and #Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph)

📽 http://www.worldsciencefestival.com/2014/09/cant-predict-math/

📄 Hidden geometric correlations in real multiplex networks

Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos

http://www.nature.com/nphys/journal/v12/n11/full/nphys3812.html

📌 ABSTRACT:
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.

Subject terms:

#Applied_physics
#Complex_networks
#Statistics

Cantor set (the mathematical equivalent of a croissant).

How does nonlinearity manufacture fractals and chaos? There is one and only one answer: #stretching and #folding. All flows and all #maps that manufacture fractals do it by stretching and folding. Let’s look at a simple example. Think of a pastry chef making a #croissant. She puts down the dough and stretches it with a rolling-pin. Then she puts a layer of butter on it and folds it. She rolls and stretches it again, puts another layer of butter, and folds it again. And so on ad infinitum, or almost. What you get is an object, a delicious croissant, which is a fractal in the direction perpendicular to the table, with a very large (quasi-infinite) number of layers. This is the way all dynamical chaos works!

🌀 برای اینکه از دنیای فرکتال‌ها سر دربیارید، نگاه کنید به نوشته فرکتال‌ها (برخال‌ها – fractals) در سیتپور:

Sitpor.org

1⃣ «مقدمه و معرفی»:
https://goo.gl/uPkpaa

2⃣ «ویژگی‌ها و تعاریف»:
https://goo.gl/SHRJ03

3⃣ «خم‌های فضاپرکن و فرکتال‌های تصادفی»:
https://goo.gl/5LE8Ck

4⃣ «مجموعه ژولیا»:
https://goo.gl/3OFlG7

5⃣ «مجموعه مندلبرو»:
https://goo.gl/g14LRc

🔵 http://thenewstack.io/how-machine-learning-works-an-overview/?utm_content=buffera722f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

🔵 https://www.quantamagazine.org/20161108-why-nate-silver-and-sam-wang-are-wrong/

A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance👇👇👇