Metadata-Version: 2.1
Name: brainwire
Version: 0.0.2
Summary: Tools to losslessly compress and decompress raw neural data.
Author: Evan Woods
Maintainer: Evan Woods
License: MIT License
        
        Copyright (c) 2024 Evan Woods
        
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Project-URL: Homepage, https://github.com/efwoods/Neuralink-Compression-Challenge
Project-URL: Issues, https://github.com/efwoods/Neuralink-Compression-Challenge/issues
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=1.0
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: signal-processing-utilities >=0.1.8

# [Neuralink Compression Challenge](https://content.neuralink.com/compression-challenge/README.html)

## Summary

[content.neuralink.com/compression-challenge/data.zip](https://content.neuralink.com/compression-challenge/data.zip) is one hour of raw electrode recordings from a Neuralink implant.

This Neuralink is implanted in the motor cortex of a non-human primate, and recordings were made while playing a video game, [like this](https://www.youtube.com/watch?v=rsCul1sp4hQ).

Compression is essential: N1 implant generates ~200Mbps of eletrode data (1024 electrodes @ 20kHz, 10b resolution) and can transmit ~1Mbps wirelessly.
So > 200x compression is needed.
Compression must run in real time (< 1ms) at low power (< 10mW, including radio).

Neuralink is looking for new approaches to this compression problem, and exceptional engineers to work on it.

## Task

Build executables ./encode and ./decode which pass eval.sh. This verifies compression is lossless and measures compression ratio.

Your submission will be scored on the compression ratio it achieves on a different set of electrode recordings.
Bonus points for optimizing latency and power efficiency

Submit with source code and build script. Should at least build on Linux.
