omnimouse-brain-model-scaling - SKILL.md Agent Skill

name: omnimouse-brain-model-scaling description: "OmniMouse methodology for multi-modal, multi-task brain models at scale. Uses 150B neural tokens dataset to study scaling properties of brain activity modeling, revealing that brain models are data-limited unlike language/vision models. Activation: OmniMouse, brain model scaling, neural tokens, multi-modal brain, mouse visual cortex, data-limited scaling."

OmniMouse: Brain Model Scaling Properties

Multi-modal, multi-task brain models trained on 150B neural tokens reveal unique scaling properties where brain modeling remains data-limited despite vast datasets.

Metadata

Source: arXiv:2604.18827
Authors: Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, et al.
Published: 2026-04-20 (ICLR 2026)
Categories: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)

Core Methodology

Dataset

Massive Scale Mouse Neural Recordings:

3.1 million neurons from visual cortex
73 mice across 323 sessions
150+ billion neural tokens
Multiple modalities: natural movies, images, parametric stimuli, behavior

Model Architecture

OmniMouse supports three flexible test-time regimes:

Neural Prediction: Predict neural responses to stimuli
Behavioral Decoding: Decode behavior from neural activity
Neural Forecasting: Predict future neural states

Any combination of the three can be used at test time.

Key Innovation

Multi-task learning across prediction, decoding, and forecasting with modality-agnostic architecture.

Key Findings

Scaling Properties

Inverts Standard AI Scaling Story:

Language/Vision: Massive datasets → parameter scaling drives progress
Brain Modeling: Models remain data-limited despite 150B tokens

Model Size vs Data

Performance scales reliably with more data
Gains from increasing model size saturate
Even mouse visual cortex (relatively simple) requires more data

Phase Transition Hypothesis

Systematic scaling raises possibility of emergent capabilities with larger, richer datasets - paralleling large language model phase transitions.

Implementation Guide

Prerequisites

Large-scale neural recording data
Multi-modal stimuli (visual, behavioral)
Computational resources for 150B+ token training

Model Design Principles

# Conceptual architecture
class OmniMouse(nn.Module):
    """Multi-modal, multi-task brain model"""
    def __init__(self):
        self.neural_encoder = NeuralEncoder()
        self.stimulus_encoder = StimulusEncoder()
        self.behavior_encoder = BehaviorEncoder()
        self.fusion_transformer = FusionTransformer()
        
    def forward(self, neural, stimulus, behavior, mode='all'):
        # Flexible test-time regimes
        outputs = {}
        if 'prediction' in mode:
            outputs['neural_pred'] = predict_neural(stimulus)
        if 'decoding' in mode:
            outputs['behavior_decoded'] = decode_behavior(neural)
        if 'forecasting' in mode:
            outputs['future_neural'] = forecast_neural(neural)
        return outputs

Training Strategy

Multi-task pretraining on all available modalities
Scale data rather than model parameters for brain modeling
Cross-subject generalization evaluation

Applications

Neuroscience Research

Large-scale neural population modeling
Cross-subject neural response prediction
Behavior-neural coupling analysis

Brain-Computer Interfaces

Neural decoding for BCIs
Multi-modal neural signal processing
Real-time neural prediction

AI-Neuroscience Bridge

Understanding what makes brain modeling unique
Data collection strategy optimization
Scaling law predictions for neural AI

Pitfalls

Common Misconceptions

Assuming brain models follow language model scaling laws
Under-investing in data collection vs. model capacity
Treating brain regions as independent prediction targets

Best Practices

Prioritize data scaling over model scaling
Design for multi-task, multi-modal objectives
Account for biological variability across subjects

Related Skills

spikingbrain2.0-foundation-models
brain-dit-fmri-foundation-model
eeg-foundation-model-adapters

Code Availability

Code available at: [GitHub repository referenced in paper]

References

Willeke et al. (2026). OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens. ICLR 2026. arXiv:2604.18827.