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
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
CONECT | Computational Neuroscience Center @ INT
CONECT | Computational Neuroscience Center @ INT.
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 [email protected] and Mathilde Bonnefond [email protected] until June 10th 2025.
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 [email protected] and Mathilde Bonnefond [email protected] until June 10th 2025.
Scientific Programming
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/982656…
vbi_demo_workshop_inference.zip
1.1 MB
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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! 🙌
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! 🙌
GitHub
GitHub - Ziaeemehr/ankideck: provide codes for building flashkards for anki Deck
provide codes for building flashkards for anki Deck - Ziaeemehr/ankideck