2025-06-12 : Workshop on Model Inversion
When: Thursday June 12th 14:00 to 18:00Where: Salle Laurent Vinay, Institut de Neurosciences de la Timone, Marseille, France.

Page Link: https://conect-int.github.io/
Zoomlink: https://univ-amu-fr.zoom.us/j/98265637982?pwd=H3XzYziirf301CBX327rFFaDbCKHW4.1

Dear all,
Have you ever asked yourself how to find the neural model that best describes your data? What a good question! For complex models, no easy solution exists. Generally, this issue is referred to as "model inversion", and it often represents an ill-posed problem in data science, where no unique solution is at hand. However, recent advances in ML and AI are providing interesting tools that can be used to perform model inversion and fit neural models to brain data.
The aim of the workshop is to provide an overview of projects focusing on model inversion. Although technical, the workshop will try to provide an overview for experimentalists and those who are not familiar with model inversion techniques.

PROGRAM

12 June 2025 (Salle Laurent Vinay, INT)
14:00 Nina Baldy (TNG-INS) - Dynamic Causal Modeling in Probabilistic Programming Languages14:45 Pedro Garcia (BraiNets-INT) - A Dynamic Causal Model to infer effective connectivity from meg induced responses (high-gamma-activity): a workflow for model bayesian inversion
15:30 Pause coffee: :mate_drink:
15:45 Cyprien Dautrevaux (BraiNets-INT) - Dynamic Causal Modelling for ERPs propagation estimated from MEG
16:30 Jean-Didier Lemaréchal (BraiNets-INT) - Bayesian inference applied to neuronal models: methods & applications
17:15 Abolfazl Ziaeemehr (TNG-INS) - Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

PhD #Position
https://elifkoksal.github.io/positions.html

Multiscale brain rhythms under healthy and epileptic conditions: computational modeling insights for clinical applications

Neural activity in the brain operates across multiple scales, encompassing both spatial and temporal dynamics. In patients with epilepsy, however, cognitive impairments are often linked to disruptions in these neural mechanisms, particularly through interictal epileptiform discharges (IEDs). This project aims to uncover new insights into the link between electrophysiology and attention deficits, one of the most prevalent cognitive impairments in patients with epilepsy, by exploring the role of IEDs. The PhD candidate will develop a comprehensive neocortical population model. The model will be validated on electrophysiological signals recorded in epileptic patients, and its dynamics will be studied to detail the mechanisms of multiple timescale interactions giving rise to healthy and pathological activity.

The research project is at the interface between computational, cognitive, and clinical neurosciences. The candidate will preferably have some background in applied mathematics or computational neuroscience/systems biology. Programming skills in Python and knowledge of dynamical systems are required. Knowledge in cognitive neuroscience, electrophysiology and/or EEG analysis would be an asset. The PhD fellow will join the Cophy Team hosted at the Center for Neuroscience Research of Lyon (CRNL), France. The ideal start date is September 2025, with some flexibility.

Candidates should send their CV, a motivation letter, contact information for 2-3 references and their master degree notes (if available) to Elif Köksal-Ersöz elif.koksal@inria.fr and Mathilde Bonnefond mathilde.bonnefond@inserm.fr until June 10th 2025.

ساخت خودکار فلش‌کارت‌های هوشمند با کمک مدل زبانی و پایتون

مدتی پیش در مورد ساخت فلش‌کارت با چت جی پی تی نوشتم.
اینجا
حالا یه قدم جلوتر رفتم و یه پکیج پایتون ساختم که کل فرایند- استخراج متن تا اصلاح و اضافه کردن صدا-رو خودش انجام می‌ده.
🔊 تفاوت مهم نسخه جدید اینه که صداها با مکث‌های طبیعی بین جمله‌ها تولید می‌شن و نتیجه خیلی روان‌تر و گوش‌نوازتر شده.
🧠 بخش اصلی تولید کارت‌ها همچنان توسط مدل زبانی (GPT) انجام میشه، و داخل پکیج یه ابزار پیشنهادی برای استفاده مستقیم از GPT هم در نظر گرفته شده تا راحت‌تر بشه کارت‌ها رو ساخت و ویرایش کرد.
📦 سورس‌کد و مستندات روی گیت‌هاب در دسترس هست:
👉 https://github.com/Ziaeemehr/ankideck/
چند ویژگی اصلی پکیج:
استخراج متن از فایل‌های PDF (حتی نسخه‌های اسکن‌شده)
تولید خودکار فلش‌کارت دو ستونه (مثلاً فرانسوی ↔ فارسی)
افزودن تلفظ با کیفیت بالا (TTS)
زمان‌بندی مکث‌ها و بهبود طبیعی بودن صداها
اگر به یادگیری زبان یا ساخت ابزارهای آموزشی با هوش مصنوعی علاقه دارید، فکر می‌کنم این پروژه می‌تونه براتون جالب باشه.
خوشحال می‌شم نظرتون رو بدونم 🙌
#AI hashtag#ChatGPT #Python #EdTech hashtag#Anki #LanguageLearning #OpenSource


Building Smart Flashcards Automatically with ChatGPT and Python
A while ago, I shared a post about creating flashcards with ChatGPT.
Now I’ve taken it a step further - I built a Python package that automates the whole process: extracting text, cleaning and structuring cards, and adding high-quality audio.
🔊 The new version generates voices with natural pauses between sentences, so the listening experience feels much smoother and more realistic.
🧠 The main part of card generation still relies on a GPT-based language model, and the package includes a suggested GPT tool that makes it super easy to create and refine your cards.
📦 You can find the source code and docs here:
👉 https://github.com/Ziaeemehr/ankideck
Main features:
Extract text from PDFs (even scanned ones)
Automatically generate bilingual flashcards (e.g., French ↔ Persian)
Add TTS audio with natural timing
Fully automated and customizable workflow
If you're into language learning or AI-powered study tools, this project might be worth checking out.
Would love to hear your thoughts! 🙌

🧠📦 vbjax_dynamics
*A JAX-based library for numerical integration of dynamical systems.*

🚀 GitHub: github.com/Ziaeemehr/vbjax_dynamics


⚙️ Features

• ODE Integration — Ordinary Differential Equations
– Efficient loop-based integrators with JIT compilation
– Full support for jax.vmap (parallel trajectories)

• SDE Integration — Stochastic Differential Equations
– make_sde(): Integration with pre-generated noise
– make_sde_auto(): Automatic noise generation from keys
– Euler–Maruyama scheme
– Fully reproducible with seeds

• DDE Integration — Delay Differential Equations
– Fixed delays supported
– History interpolation

• SDDE Integration — Stochastic Delay Differential Equations
– Combine stochastic and delayed dynamics

• JAX-Native Design
– Fully JIT-compiled
– Auto-differentiable
– GPU/TPU compatible
– Pure functional API

💡 Installation

pip install vbjax_dynamics

PhD Position in Computational & Systems Neuroscience (Marseille, France): Neuromodulatory control of predictive processing in visual cortical circuits
⏰️ Deadline: January 28, 2026

Dear colleagues,

Applications for non-EU students are now open for the Master 2 in Computational Neuroscineces (CNS) at the University of Lyon (France). See our website for more details: https://masterneuro.univ-lyon1.fr/m2-cns/

The Lyon neurograduate school also offers fully funded scholarships to enrol in the CNS program or in other neurosciences training paths. Deadline : 23/01/2026, more information here : https://neurograduate.univ-lyon1.fr/fellowships/

The CNS program in Lyon trains students in modern computational and analytical methods to study the brain: from the electrical activity of single neurons to neural networks, interacting brain regions, and animal behavior. Students learn the principles of brain function by applying statistics, signal processing, and computational modeling to real neuroscientific datasets, gaining extensive hands-on experience through practical Python-based work. The main objective of CNS is to equip students with the skills and critical perspective needed to design, conduct, and interpret analyses of electrophysiological and behavioral data from a mechanistic point of view.



Contact info: Matteo Di Volo (matteo.di-volo@univ-lyon1.fr) and Jeremie Mattout (jeremie.mattout@inserm.fr)

Conduit is a lightweight relay server used by Psiphon that helps users in censored networks access the open internet by securely forwarding their traffic through volunteer-run nodes outside the censorship zone.
(for LINUX)

docker run -d --name conduit \
  --cpus="2.0" \
  --memory="2g" \
  --ulimit nofile=65536:65536 \
  -v conduit-data:/home/conduit/data \
  --restart unless-stopped \
  --log-driver local --log-opt max-size=10m --log-opt max-file=5 \
  ghcr.io/ssmirr/conduit/conduit:latest start \
  -b 5 -m 40 2>&1

To see how many clients is connected and other details:

docker logs conduit --tail 1

Github:
https://github.com/Ziaeemehr/JaxCont


Docs:
https://jaxcont.readthedocs.io/en/latest

Github: https://github.com/Ziaeemehr/lyapax
Docs:
https://lyapax.readthedocs.io/en/latest

🧠 New release: ModelingNeuralDynamics v1.0.0 is now on PyPI!

This is an open-source Python port of the code behind An Introduction to Modeling Neuronal Dynamics by Christoph Borgers, a widely used text for computational neuroscience courses, originally accompanied by MATLAB programs.

What's new:

📦 pip install modelingneuraldynamics, the shared helper package is now a real PyPI release, not just a clone-and-hope setup.

📓 22 chapters converted to Jupyter notebooks, single-neuron models, bifurcations, synaptic dynamics, network rhythms (PING/ING), and STDP, each as one self-contained, tested notebook.

🎛️ Interactive widgets, several notebooks use ipywidgets so you can drag sliders and watch firing rates, bifurcations, and phase planes respond live, instead of re-running cells by hand.

▶️ One-click Colab, every converted notebook opens directly in Google Colab and installs its own dependencies, no local setup required.

✅ CI-tested, every chapter runs in automated tests on every change, so the examples you open are the examples that actually run.

Whether you're teaching, TA-ing, or just curious how a Hodgkin-Huxley neuron becomes a gamma-rhythm network, it's free to explore:

🔗 GitHub: github.com/ITNG/ModelingNeuralDynamics
🔗 PyPI: pypi.org/project/modelingneuraldynamics

#ComputationalNeuroscience #OpenSource #Python #Neuroscience #Jupyter #ScientificComputing