A tutorial video on writing applied-mathematics papers.

In case it is helpful, here it is: https://t.co/OwLNGCmQwS

Dive into Deep Learning
Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

https://d2l.ai/index.html

JOB DESCRIPTION
The EEG-BCI facility of the Fondation Campus Biotech Geneva (FCBG) offers state-of-the-art equipment and high-level expertise in EEG and BCI to these labs to give them the best possible environment to conduct their experiments.

REQUIRED PROFILE
Qualifications
• PhD in computer science, neuroscience or related field
• Strong experience in EEG BCI and/or neurofeedback
• Experience in human EEG research 
• High skills with software development, including graphical interface and multi-OS porting
• High programming skills in Python. Matlab and C++ appreciated.
• Proficiency in English. French appreciated.
Application: Applications should include a CV, a cover letter and reference letters. The application should be sent by email to: administration@fcbg.ch
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A young filmmaker sets out to document a brilliant neuroscientist who has become frustrated with his field’s status quo. With time elapsing and millions of dollars on the line, In Silico explores an audacious 10-year quest to simulate the entire human brain on supercomputers. Along the way, it reveals the profound beauty of tiny mistakes and bold predictions — a controversial space where scientific process meets ego, and where the lines between objectivity and ambition blur.

Director: Noah Hutton

#HumanBrainProject

INFO:
The documentary premiered on 30th of April 2021 to a broader audience of US citizens exclusively. In awe of Henry Markram's idealist approach to understand the brain against our better materialist judgement I share the documentary to an european audience for educational purposes.

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#spare_time
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It’s become commonplace to record from hundreds of neurons simultaneously. If past trends extrapolate, we might commonly record 10k neurons by 2030. What are we going to do with all this data?
.....
by: Patrick Mineault

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REGULARIZATION: An important concept in Machine Learning

The word regularize means to make things regular or acceptable. This is exactly why we use it for. Regularizations are techniques used to reduce the error by fitting a function appropriately on the given training set and avoid overfitting. Now to get a clear picture of what the above definition means, let’s get into the details.

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#basic_maths

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Mathematical Methods in Computational Neuroscience

Computational Neuroscience and Inference from data are disciplines that extensively use tools from Mathematics and Physics to understand the behavior of model neuronal networks and analyze data from real experiments. Due to its interdisciplinary nature and the complexity of the neuronal networks, the list of techniques that are borrowed from Physics and Mathematics is an extensive one. Although using tools from standard curriculum of Physics, Mathematics and Engineering is common, more advanced research requires methods and techniques that are not usually covered in any single discipline. 
 
To fill in this gap, this summer school covers some of the most important methods used in computational neuroscience research through both main lectures and scientific seminars (5-6 main lectures per topic and  1-2 seminars by each invited seminar speaker)

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Deep Learning, which is a course on the theory and techniques of deep learning with an emphasis on neuroscience. The course runs from August 2-20.
The syllabus for this course is still in progress, here is the current draft.

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The Simplest Math Problem No One Can Solve

The Collatz Conjecture is the simplest math problem no one can solve — it is easy enough for almost anyone to understand but notoriously difficult to solve.

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Machine learning, computer vision, statistics and general scientific computing for .NET

https://github.com/accord-net/framework
#ML

Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations...

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#paper
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Post-doctoral position:

Research Fellow, UCL Department / Division UCL Queen Square Institute of Neurology Specific unit / Sub department Wellcome Centre for Human Neuroimaging, Max Planck UCL Centre for Computational Psychiatry and Ageing
Location of position:
London
Grade 7
Hours Full Time
Salary (inclusive of London allowance) £36,028 - £43,533 per annum
Duties and Responsibilities
Applications are invited for a Research Fellow in the Max Planck UCL Centre for Computational Psychiatry and Ageing Research to undertake high quality research and produce high-impact publications in the context of the ERC-funded research project "Action selection under threat - the complex control of human defence" led by Dr Dominik Bach.

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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...

#spare_time

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A Student's Guide to Python for Physical Modeling: Second Edition
#python
#book
#beginner

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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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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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/

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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