Topic
neuroscience
The Apple FaceID Veteran Building a Frontier AI Model for the Human Brain
Gidi Littwin, co-inventor of Apple's FaceID and Vision Pro, has spent six years building frontier AI model startup Hemispheric. With $52M in funding and data from 100,000 brains, the company aims to diagnose PTSD, Alzheimer's, and depression using non-invasive EEG headsets and deep learning.
REST-GAN: A Deep Generative Model for Resting-State EEG Synthesis and Transferable Representation Learning
Researchers introduce REST-GAN, a generative adversarial network for resting-state EEG that both synthesizes realistic neural signals and learns transferable representations. The model achieves high precision and recall in band-power features and shows competitive performance in demographic classification tasks, requiring substantially less training data and computational resources than existing methods.
OmniMouse Brain Model Trained on 150 Billion Neural Tokens Reveals Unusual Scaling Laws
Researchers trained OmniMouse, a multi-modal, multi-task brain model, on 150 billion neural tokens from 3.1 million mouse visual cortex neurons. The model achieves state-of-the-art performance across neural prediction, behavioral decoding, and neural forecasting. Scaling analysis shows performance improves with more data but gains from increasing model size saturate, contrasting with language and vision AI.
New EEG Benchmark Promises Standardized Evaluation of Foundation Models
A new benchmark called EEG-FM-Bench aims to standardize evaluation of electroencephalography foundation models (EEG-FMs). It integrates 14 datasets across 10 paradigms and provides tools for gradient and representation analysis. Early experiments reveal critical insights about multi-task learning, pre-training efficiency, and model scaling.
EEGNet Study Reveals Key Limitations in fNIRS Cognitive Load Classification
A comprehensive study published on arXiv systematically evaluates EEGNet for classifying cognitive load from fNIRS signals. The research highlights critical challenges in generalization, achieving only 56.11% accuracy under subject-independent evaluation, and underscores the importance of segmentation strategy and learning rate selection.
Cortical Geometry and Wiring Serve as Powerful Inductive Biases for Recurrent Neural Networks
A new study leveraging the MICrONS functional connectomics dataset demonstrates that recurrent neural networks initialized with cortical geometry, wiring, and functional relationships consistently outperform baseline and partially constrained models across three decision-making tasks, achieving lower entropy and modular organization.
Subject-Specific Encoders Improve Cross-Subject EEG Decoding, Study Finds
A new study on arXiv.org proposes replacing shared EEG encoders with subject-specific encoders to handle inter-subject distribution shifts. The hybrid model, tested on four motor-imagery datasets, internalises Euclidean Alignment and increases class distinctiveness, though head selection for unseen subjects remains a bottleneck.
Technology Jeff Bezos Funds Flourish's $2.5 Billion Quest for a Synthetic Brain That Runs on 50 Watts
Jeff Bezos has invested $50 million (later nearly doubled) into Flourish, a neuro AI startup founded by former Amazon executive Rob Williams and neuroscientist Thomas Reardon. The company aims to build a synthetic intelligence system called Cortex AI that matches the human brain's learning efficiency and power budget of 50 watts or less, addressing the energy inefficiency of large language models. Flourish has raised $500 million at a $2.5 billion valuation from investors including Lux Capital, Google Ventures, and Catalio.