-
Accelerated Methods in {Multi-Modal, Multi-Metric, Many-Model} CogNeuroAI
A tutorial showcasing a number of (GPU-accelerated) methods for probing the representational alignment of brains, minds, and machines. An exploration of computational modeling methods at the intersection of cognitive neuroscience and artificial intelligence.
-
An Overview of the Neuropixels Visual Behavior Dataset From the Allen Institute
Tutorial on analyzing the Visual Behavior Neuropixels dataset from the Allen Institute
-
CONFORM: A Project to Create Crowd-Sourced Open Neuroscience fMRI Foundation Models
We propose CONFORM (Crowd-Sourced Open Neuroscience fMRI Foundation Model), a project that will bring together recent advances in neural data processing and analysis with a novel, crowd-sourced infrastructure. This transformative approach will overcome several current challenges in creating a foundational human fMRI model for vision: collecting massive amounts of data from a handful of participants is neither scalable nor sustainable; the number of participants is small for such datasets; stimulus diversity is limited; and generalizability to different populations is poor. CONFORM will overcome these limitations by combining a powerful denoising method (PSN), a scalable framework for aggregating existing fMRI datasets (MOSAIC), and a meta-learning model that enables generalization with much smaller data from new participants (BraInCoRL). Our collaborative effort will produce models built on unprecedented scale and diversity—ultimately with hundreds of participants and hundreds of thousands of naturalistic image and movie stimuli—and provide the tools for continuous expansion of the underlying dataset. This ``crowd-sourced'' approach will allow many more researchers to leverage state-of-the-art NeuroAI methods using the scale of data they typically collect, democratizing access to powerful models and accelerating scientific discovery for a wide range of neuroscientific domains and populations.
-
Neural Keyword Spotting on LibriBrain
End-to-end walkthrough for Neural Keyword Spotting (KWS) on the LibriBrain MEG corpus: load data, frame the task, train a compact baseline, and evaluate with precision–recall metrics tailored to extreme class imbalance.
-
NLDisco: A Pipeline for Interpretable Neural Latent Discovery
-
Reading Observed At Mindless Moments (ROAMM): A Simultaneous EEG and Eye-Tracking Dataset of Natural Reading with Attention Annotations
ROAMM (Reading Observed At Mindless Moments) is a large-scale multimodal dataset of EEG and eye-tracking during naturalistic reading, with precise annotations of mind-wandering episodes to advance research in attention, language, and machine learning.