int-brain-lab

int-brain-lab / ibl-ai-agent

Public

IBL AI Agent helps you use a coding agent such as OpenAI Codex to analyze International Brain Laboratory (IBL) data.

14
3
100% credibility
Found May 22, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

IBL AI Agent is a scientific research tool that helps neuroscientists analyze brain activity data from the International Brain Laboratory. It works with AI coding assistants to let scientists ask questions about neural data, explore findings interactively, and generate analysis notebooks with code and visualizations. The tool follows careful scientific methodology by separating data exploration from confirmatory analysis to avoid false conclusions. Scientists can review results privately or publish reports to the web after privacy checks. The project emphasizes that AI assistance does not replace scientific judgment—researchers are responsible for verifying all findings before trusting or publishing them.

How It Works

1
🧬 Discover brain research data

You learn about a powerful tool that helps scientists analyze brain activity data using AI assistance.

2
📋 Set up your research project

You download the project and start an AI coding assistant that understands neuroscience data.

3
❓ Ask your scientific question

You type a question about brain activity patterns, and the AI starts planning how to answer it.

4
🔬 Explore and refine together

You work with the AI to explore the data, adjust your analysis, and make sure the approach is sound.

5
📊 Get your analysis notebook

The AI writes code, creates visualizations, and produces a complete analysis notebook for you to review.

6
Choose your next step
🔍
Keep it private

Review the notebook and results on your own computer, keeping everything confidential.

🌐
Publish publicly

After checking for any private information, publish a report to share your findings.

🎉 Your research is complete

You've analyzed brain data, created visualizations, and optionally shared your findings with the scientific community.

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AI-Generated Review

What is ibl-ai-agent?

IBL AI Agent is a Python tool that lets you use coding agents like OpenAI Codex or Claude Code to analyze neuroscience data from the International Brain Laboratory. You clone the repo, start a coding agent inside it, and ask questions about brain activity data. The agent writes analysis code, generates plots, produces reports, and can even publish those reports to GitHub Pages. It works with the IBL Brain Wide Map dataset, a compressed collection of neural spike times and behavioral data that fits under 10 GB.

The workflow is designed for interactive scientific work: the agent clarifies your question, splits data into exploration and confirmation sets to prevent p-hacking, helps you refine metrics and statistical tests on the exploration set, then locks your approach before running confirmatory analysis. You get a Jupyter notebook with all the code and a final HTML report.

Why is it gaining traction?

The hook is clear: neuroscientists can now explore large-scale neural datasets through natural language questions instead of writing code from scratch. The strict exploration/confirmation data split is a genuine scientific safeguard that is still rare in systems neuroscience. The agent generates working Python code using standard IBL libraries (ONE API, brainbox, iblatlas), so the output is immediately usable. The CLI handles everything from data prefetching to report publishing, which reduces friction for labs that want reproducible workflows.

Who should use this?

Neuroscientists working with IBL data who want to quickly test hypotheses without writing boilerplate data-loading code. Labs that need reproducible analysis pipelines with built-in safeguards against statistical p-hacking. Researchers who want to publish interactive HTML reports alongside their findings. This is not for general neurodata or raw video/processing pipelines.

Verdict

This is a promising but early-stage project with only 14 stars and a 1.0% credibility score. The scientific workflow design is thoughtful, but the codebase is explicitly a work in progress with limited documentation and test coverage. If you work with IBL data and want to experiment with AI-assisted analysis, it is worth a look, but do not rely on it for production pipelines or published results without careful review. The scientist-in-the-loop philosophy is sound, but the implementation needs more battle-testing before mainstream adoption.

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