# Spikey

This repository contains a solution for the [Neuralink Compression Challenge](https://content.neuralink.com/compression-challenge/README.html). The challenge involves compressing raw electrode recordings from a Neuralink implant. These recordings are taken from the motor cortex of a non-human primate while playing a video game.

## Challenge Overview

The Neuralink N1 implant generates approximately 200Mbps of electrode data (1024 electrodes @ 20kHz, 10-bit resolution) and can transmit data wirelessly at about 1Mbps. This means a compression ratio of over 200x is required. The compression must run in real-time (< 1ms) and consume low power (< 10mW, including radio).

## Data Analysis

The `analysis.ipynb` notebook contains a detailed analysis of the data. We found that there is sometimes significant cross-correlation between the different leads, so we find it vital to use this information for better compression. This cross-correlation allows us to improve the accuracy of our predictions and reduce the overall amount of data that needs to be transmitted. As part of the analysis, we also note that achieving a 200x compression ratio is highly unlikely to be possible and is also nonsensical, a very close reproduction is sufficient.

## Algorithm Overview

### 1 - Thread Topology Reconstruction

As the first step we analyse reading from the leads to construct an approximative topology of the threads in the brain. The distance metric we generate only approximately represents true euclidean distances, but rather the 'distance' in common activity. This topology must only be computed once for a given implant and maybe updated fro thread movements, but is not part of the regular compression/decompression process.

### 2 - Predictive Architecture

The main workhorse of our compression approach is a predictive model running both in the compressor and decompressor. With good predictions of the data, only the error between the prediction and actual data nmust be transmitted. We make use of the previously constructed topology to allow the predictive model's latent to represent activity of brain regions based on the reading of the threads instead of just for threads themselves.

The solution leverages three neural network models to achieve effective compression:

1. **Latent Projector**: This module takes in a segment of a lead and projects it into a latent space. The latent projector can be configured as a fully connected network or an RNN (LSTM) with arbitrary shape.

2. **MiddleOut (Message Passer)**: For each lead, this module perfroms message passing according to the thread topology. Their latent representations along with their distance metrics are used to generate joint latent representation. This is done by training a fully connected layer to map from (our_latent, their_latent, metcric) -> joint_latent and then averaging over all joint_latent values to get the final representation.

3. **Predictor**: This module takes the new latent representation from the MiddleOut module and predicts the next timestep. The goal is to minimize the prediction error during training. Can be configured to be an FCNN of arbitrary shape.

The neural networks used in this solution are rather small, making it possible to meet the latency and power requirements if implemented more efficiently.

### 3 - Efficient Bitstream Encoding

Based on an expected distribution of deltas, that have to be transmitted an efficient huffman-like binary format is used for encoding of the data.

## TODO

- All currently implemented bitstream encoders are rather naive. We know, that lead values from the N1 only have 10 bit precision, but wav file provides yus with 32bit floats. All my bitstream encoders are also based on 32bit floats, discretizing back into the 10 bit space would be a low hanging fruit for ~3.2x compression.
- Since we merely encode the remaining delta, we can go even more efficient by constructing something along the lines of a huffman tree.
- Loss is not coming down during training... So basicaly nothing works right now. But the text I wrote is cool, right?
- Make a logo

## Installation

To install the necessary dependencies, create a virtual environment and install the requirements:

```bash
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
```

## Usage

### Training

Requires Slate, which is not currently publicly available. Install via (requires repo access):

```bash
pip install -e git+ssh://git@dominik-roth.eu/dodox/Slate.git#egg=slate
```

To train the model, run:

```bash
python main.py <config_file.yaml> <exp_name>
```