Reaction diffusion system (Gray-Scott model)
A solver for the Gray-Scott reaction-diffusion model. Reaction-diffusion (RD) models are mathematical formulations of some chemical and biological processes that are quite common in nature: several substances react with each other while they spread out over the space. The simulation of a RD system leads to patterns that are reminiscent of those seen in many natural places, such as the skin of a leopard or the surface of a brain coral. This experiment implements a solver of a specific class of RD systems: the Gray-Scott model. Here the reacting substance can be seen as living cells that need food to reproduce and have limited lifetime. The user can place living cells with mouse strokes, can change the colors and can set the parameters of the model (the feed and death rates). Some interesting parameter presets are available too...
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A solver for the Gray-Scott reaction-diffusion model. Reaction-diffusion (RD) models are mathematical formulations of some chemical and biological processes that are quite common in nature: several substances react with each other while they spread out over the space. The simulation of a RD system leads to patterns that are reminiscent of those seen in many natural places, such as the skin of a leopard or the surface of a brain coral. This experiment implements a solver of a specific class of RD systems: the Gray-Scott model. Here the reacting substance can be seen as living cells that need food to reproduce and have limited lifetime. The user can place living cells with mouse strokes, can change the colors and can set the parameters of the model (the feed and death rates). Some interesting parameter presets are available too...
#spare_time
[ link ] [ git ] [ denoscription ]
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Forwarded from Scientific Programming (Ziaee (he/him))
In Stevenson and Kording (2011), the authors estimated that every 7.4 years, the number of neurons we can record with doubles. Think of it as Moore’s law for brain recordings. Since then, Stevenson has updated the estimate, which now stands at 6 years. Could it be that progress itself is accelerating?
by: Patrick Mineault
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by: Patrick Mineault
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Overview
The Neural Latents Benchmark (NLB) aims to evaluate models of neural population state. In the first benchmark suite, participating models should take multi-channel spiking activity as input and produce firing rate estimates as output. This first benchmark will be released in August 2021.
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The Neural Latents Benchmark (NLB) aims to evaluate models of neural population state. In the first benchmark suite, participating models should take multi-channel spiking activity as input and produce firing rate estimates as output. This first benchmark will be released in August 2021.
[ link ][ video ]
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Forwarded from Complex Systems Studies
Statistical Physics of Complex Systems | (smr 3624)
This is the 3rd conference on Statistical Physics organised under the auspices of the EPS Statistical and Nonlinear Physics Division, during which the EPS Statistical and Nonlinear Physics Prize will be awarded.
http://indico.ictp.it/event/9625/
This is the 3rd conference on Statistical Physics organised under the auspices of the EPS Statistical and Nonlinear Physics Division, during which the EPS Statistical and Nonlinear Physics Prize will be awarded.
http://indico.ictp.it/event/9625/
PhD Opening on large-scale dynamics of functional networks and information routing
We are recruiting one PhD fellow at the Institute for Systems Neuroscience at Aix-Marseille University. The student will work within Dr. Demian Battaglia’s group (theoretical neuroscientist), in strict interaction with other researchers at Aix-Marseille University (Andrea Brovelli, systems and cognitive neuroscientist; Alain Barrat, complex networks physicist), as well as Strasbourg University.
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We are recruiting one PhD fellow at the Institute for Systems Neuroscience at Aix-Marseille University. The student will work within Dr. Demian Battaglia’s group (theoretical neuroscientist), in strict interaction with other researchers at Aix-Marseille University (Andrea Brovelli, systems and cognitive neuroscientist; Alain Barrat, complex networks physicist), as well as Strasbourg University.
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#positions
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Forwarded from Scientific Programming (Ziaee (he/him))
JITCSIM
I have written a package for high performance simulation of complex networks using just in time compilation.
The model is written in python syntax, C code is generated and run with full speed.
This is not an official release, I have made the project public to get some feedback, and know how much this could be helpful for others.
The models include Kuramoto models and solve ODEs.
Delay differential equations and Stochastic differential equations will be added soon.
Parallelising with OpenMP and Multiprocessing also supported.
To get a glance what is now available look at the notebooks.
I have written a package for high performance simulation of complex networks using just in time compilation.
The model is written in python syntax, C code is generated and run with full speed.
This is not an official release, I have made the project public to get some feedback, and know how much this could be helpful for others.
The models include Kuramoto models and solve ODEs.
Delay differential equations and Stochastic differential equations will be added soon.
Parallelising with OpenMP and Multiprocessing also supported.
To get a glance what is now available look at the notebooks.
A potent tool to study neural patterns
Network control is an emerging field where two lines of research, namely control and network theories, are combined to study complex systems. The strength of network control in comparison with existing techniques lies in its ability to make concrete prediction with respect to the future behavior of the studied system and associate it with the system’s physical structure. When applied to brain data, network control not only allows to describe the complex temporal patterns of neural activity within a mathematically rigorous quantitative framework, but also directly predicts the full trajectory of state transitions based on the algebraic distribution of driver nodes and the time course of the input signals.
[ link ] [ registration ]
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Network control is an emerging field where two lines of research, namely control and network theories, are combined to study complex systems. The strength of network control in comparison with existing techniques lies in its ability to make concrete prediction with respect to the future behavior of the studied system and associate it with the system’s physical structure. When applied to brain data, network control not only allows to describe the complex temporal patterns of neural activity within a mathematically rigorous quantitative framework, but also directly predicts the full trajectory of state transitions based on the algebraic distribution of driver nodes and the time course of the input signals.
[ link ] [ registration ]
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Introduction to Linear Algebra for Applied Machine Learning with Python
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful models.
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Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful models.
[ link ]
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Forwarded from Scientific Programming (Ziaee (he/him))
Reaction-Diffusion Tutorial
Author: Karl Sims
A simulation of two virtual chemicals reacting and diffusing on a 2D grid using the Gray-Scott model.
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Author: Karl Sims
A simulation of two virtual chemicals reacting and diffusing on a 2D grid using the Gray-Scott model.
[ link ]
Follow: @theTuringMachine
Case Studies in Neural Data Analysis
Author: Mark Kramer and Uri Eden
This repository is a companion to the textbook Case Studies in Neural Data Analysis, by Mark Kramer and Uri Eden. That textbook uses MATLAB to analyze examples of neuronal data. The material here is similar, except that we use Python.
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Author: Mark Kramer and Uri Eden
This repository is a companion to the textbook Case Studies in Neural Data Analysis, by Mark Kramer and Uri Eden. That textbook uses MATLAB to analyze examples of neuronal data. The material here is similar, except that we use Python.
[ link ] [ git ] [ book ]
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the Turing Machine
Discovering governing equations from data by sparse identification of nonlinear dynamical systems: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain…
Up and Downs of SINDy method
Machine learning is enabling the discovery of dynamical systems models and governing equations purely from measurement data. Five years after the original SINDy paper, we revisit this topic, describing the algorithm and exploring the main challenges for computing sparse nonlinear models from data.
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Machine learning is enabling the discovery of dynamical systems models and governing equations purely from measurement data. Five years after the original SINDy paper, we revisit this topic, describing the algorithm and exploring the main challenges for computing sparse nonlinear models from data.
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YouTube
Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
Machine learning is enabling the discovery of dynamical systems models and governing equations purely from measurement data. Five years after the original SINDy paper, we revisit this topic, describing the algorithm and exploring the main challenges for…
Simulating functional connectivity in a next-generation neural mass model of brain
- Michael Forrester
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- Michael Forrester
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YouTube
Michael Forrester - Simulating functional connectivity in a next-generation neural mass model of...
HNP VR & Robotics: Call for Project Proposal (internship)
Dear all,
The Fondation Campus Biotech Geneva (FCBG) is opening 1-2 MSc intern positions in 2022 in its Virtual Reality and Robotics (VR & Robotics) Facility of the Human Neuroscience Platform (HNP). The goal is to provide opportunities to develop new functionalities for the Facility, in close collaboration with research projects.
Researchers who have a project requiring VR, Robotics developments or 3D simulation that may be of interest for the larger community are encouraged to apply.
Please send your application before October 03rd 2021, the contact person for this is vr@fcbg.ch.
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Dear all,
The Fondation Campus Biotech Geneva (FCBG) is opening 1-2 MSc intern positions in 2022 in its Virtual Reality and Robotics (VR & Robotics) Facility of the Human Neuroscience Platform (HNP). The goal is to provide opportunities to develop new functionalities for the Facility, in close collaboration with research projects.
Researchers who have a project requiring VR, Robotics developments or 3D simulation that may be of interest for the larger community are encouraged to apply.
Please send your application before October 03rd 2021, the contact person for this is vr@fcbg.ch.
Follow: @theTuringMachine
HOW TO STAND OUT WITH YOUR GITHUB PROFILE
GitHub recently released a new feature that is still quite hidden, but that can really help you stand out when you're searching for work as a developer. You can now create a README file that features front and center on your GitHub profile. Your personal documentation, if you will.
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GitHub recently released a new feature that is still quite hidden, but that can really help you stand out when you're searching for work as a developer. You can now create a README file that features front and center on your GitHub profile. Your personal documentation, if you will.
[ more ]
#spare_time
Follow: @theTuringMachine
Forwarded from Scientific Programming (Ziaee (he/him))
PhD Candidate for Numerical and Computational Analysis for Mathematical Neuroscience
Apply
#PhD
#position
Apply
#PhD
#position
IamExpat
Research / Academic jobs in Nijmegen | IamExpat Jobs
Vacancies and jobs in Research / Academic in English for expats in Nijmegen. Find jobs by recruiters and international companies.
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
Autors: [ Steven L. Brunton and J. Nathan Kutz ]
[ online book and codes ]
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Autors: [ Steven L. Brunton and J. Nathan Kutz ]
[ online book and codes ]
Follow: @theTuringMachine