Danilo Bzdok | Mila

The brain in the machine: How AI could help explain how we think | IBM

Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.

AI: A Guide for Thinking Humans | Melanie Mitchell | Substack

I write about interesting new developments in AI. Click to read AI: A Guide for Thinking Humans, by Melanie Mitchell, a Substack publication with tens of thousands of subscribers.

The Fabric and the Brain

Articulating agent ecologies with high-personality planetary computation

NeuroAI

Neuroscience, cognitive science, and AI are all questing for principles that help generalization. Learn more through a live, synchronous program designed for focused, hands-on learning.

The Global Brain Algorithm

<p>This is a collection of explanations on the global brain algorithm. We explain core concepts in simple terms that help to understand the algorithms as a whole.</p>

GitHub - AIM-KannLab/BrainIAC | Giovanni Giulietti

One of the biggest hurdles in neuroimaging AI has always been the scarcity of labeled data, especially for rare conditions. But a recent study published in Nature Neuroscience introduces a potential solution: BrainIAC (Brain Imaging Adaptive Core). Unlike traditional models that are trained for one specific task, BrainIAC is a "foundation model", a large-scale AI model trained on vast amounts of data that can perform many different tasks and be adapted to specific applications. It was pretrained on a massive dataset of 48965 brain structural MRI sequences (T1w, T2w, FLAIR, T1CE) using self-supervised learning, allowing it to understand the "big picture" of brain anatomy across different ages and conditions. It has been tested across seven diverse and clinically meaningful applications:  - brain age prediction - IDH mutation classification - Mild Cognitive Impairment (MCI) classification - diffuse glioma overall survival prediction - MR sequence classification - time-to-stroke prediction - tumor segmentation  BrainIAC consistently outperformed traditional supervised learning approaches and other medical-specific models. The real "magic" happens in low-data scenarios. While traditional AI usually needs thousands of examples, BrainIAC can adapt to new tasks using only a few samples.  BrainIAC can be used for structural brain MRI analysis through two main methods: - Web Usage (BrainIAC platform): the easiest way to use BrainIAC, providing a user-friendly interface to upload structural brain MRI data and run inference. - Local usage (GitHub repository): for more advanced and customized analysis. It allows complete control over the data preprocessing, model training, and inference pipelines. It requires Python 3.9+ and NVIDIA GPU with CUDA 11.0+. 🔗 BrainIAC paper:    https://lnkd.in/dq6Gprgt 🔗 BrainIAC web platform https://lnkd.in/dpreadMp 🔗 BrainIAC GitHub repo: https://lnkd.in/duwSEg4V #MedicalAI #Neuroscience #HealthTech #BrainIAC #Radiology #Innovation #DeepLearning #PrecisionMedicine

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

How AI coding is reshaping theoretical neuroscience

Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Neuroscientists weigh in on that tectonic change.

Learning Outside the Brain: Integrating Cognitive Science and Systems Biology

Learning is commonplace in organisms such as ourselves and even in organisms as far distant as the bee and the octopus. Such learning is implemented by brains, or neuronal networks, and has been extensively studied within ethology, psychology, cognitive science, and neuroscience. Whether learning also takes place in nonneuronal settings has remained a matter of sustained controversy, too often dominated by ideological views. In this survey, I will explain how learning can be rigorously interpreted as a form of information processing and then explore the evidence for whether learning also takes place in organismal contexts outside the brain, such as physiology, development, and individual cells. I will try to explain why it is important to build bridges in this way between cognitive science and systems biology, why concepts and methods from various branches of engineering may be helpful in this task, and what the eventual impact may be on how we think about the organism.

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI

Computational Psychiatry Needs Systems Neuroscience

This piece was originally published in The Transmitter. I am adapting it here with some additional context for readers of this newsletter.

#digitalbrainproject #neuroscience #artificialintelligence #openscience #callforprojects #research #ai #brain | Fondation Adolphe de Rothschild

The Rothschild Foundation Hospital is excited to launch the Digital Brain Project, a $5M collaborative research initiative to build an open source foundational model of the brain through open-access neural data.  Apply by May 15th for up to $500k in funding. Help crack the neural code 👉digitalbrainproject.org  Our objective is to develop a standardized, publicly available dataset of human brain recordings to accelerate fundamental research in cognitive neuroscience, AI system development, and clinical health. Our independent, multidisciplinary Scientific Committee oversees data collection, ethical review, privacy, and open science practices of our project — setting standards for large-scale brain research. The Digital Brain Project is conceived as an open collaborative infrastructure, at the intersection of neuroscience, artificial intelligence, and data science. It aims to accelerate data development, a key bottleneck to developing a foundational AI model of the human brain.  Key Focus Areas: 1️⃣ Brain recording data related to visual processing, language functions and cognitive actions 2️⃣ Rigorous quality, privacy, and ethical control, along with procedures for standardizing data across sites 3️⃣ Support for teams selected by an international multidisciplinary Scientific Committee Call for applications: Research teams working in neuroscience, neuroimaging, or artificial intelligence are invited to submit their applications by May 15, 2026. Selected teams will receive up to $500k USD funding to support their work. We appreciate our Scientific Committee (Lune Bellec Anne-Marie Kermarrec Arthur Mensch Russell Poldrack Lucia Melloni Jose Alain Sahel) for their independent scientific leadership, and partners Université de Montréal and AI at Meta for collaborating on this exciting project. #DigitalBrainProject #Neuroscience #ArtificialIntelligence #OpenScience #Callforprojects #Research #AI #Brain --------------------------------------------------------------  Pierre Bourdillon Julie Boyle Elisa Cascardi Jean-Rémi King Guillaume Le Hénanff Amélie Yavchitz Julien Gottsmann  Fabrice VERRIELE  Charlotte Cardin-Taillia

BrainMind

At this important juncture in neuroscience, the BrainMind ecosystem is combining the most talented of these groups in novel ways to collectively shepherd, support, and fund ideas that can be translated from labs into interventions that will benefit humanity.