Home | Neuroscience Inspired AI

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.

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.

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.

AI, Ethics, and Society — Home

Yneuro | The startup behind Neuro ID®

Imagine a world where technology and human experience blend seamlessly. At Yneuro, we’re building that world. As pioneers in neurotechnology, we craft AI-driven software solutions that transcend global barriers. Celebrated in startup ecosystems and competitions, Yneuro is redefining digital interaction, making advanced technologies intuitive and accessible for everyone. Join us as we merge technology and human cognition, creating unprecedented possibilities.

Why A Neurotech VC Bet On Anthropic: Intelligence, Interpretability, and the NeuroAI Stack — Kaleida Capital

Why would a neurotech VC bet on Anthropic? This article examines how frontier AI, neuroscience, model behavior, and intelligent systems intersect within Kaleida Capital’s NeuroAI thesis and the infrastructure shaping next-generation intelligence.

AI for science: What can it do? Can it do things? Let’s find out!

A what we’re reading spotlight

From Genius Science to Scenius Science - Cosmik Labs

Why personal AI tools might not be all we need to revolutionize science

Neural Computer: A New Machine Form Is Emerging

A research essay on Neural Computer: how it differs from agents, world models, and conventional computers; what runtime and CNC would mean; what current prototypes already show; and how software and hardware might change.

Synaptrix Labs | AI powered Brain-computer interface Solutions

Introducing Neuralis by Synaptrix Labs, the first device that allows paralyzed patients to move their wheelchairs through brain activity alone, powered by our novel AI based EEG signal processing model that makes it possible.

Jeff Bezos Is Funding a Wild Hunt for the Brain’s ‘Core Algorithm’

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

#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

AI That Evolves in the Wild | Edge.org

I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.

Embracing the suboptimal organization of the human brain

Human brain architecture is guiding brain-inspired artificial intelligence (AI) and has been treated as an optimal template, whose deviations could mark different psychiatric and neurological conditions. We argue this premise is wrong: under any single goal (e.g., minimal wiring cost or maximal communication efficiency), the human connectome is suboptimal. Instead, its organization reflects multi-objective trade-offs navigated over evolution and development under biological and environmental constraints. For psychopathology, atypical trajectories are not distances from an ideal brain but reweighted compromises in the same trade-off space. For neuro-AI, directly duplicating the brain’s connectivity risks copying its irrelevant compromises. Treating brains and models as products of multi-objective optimization and co-tuning relevant objectives offers a more powerful framework for interpreting clinical phenotypes and designing next-generation AI.