77 lines
2.7 KiB
Markdown
77 lines
2.7 KiB
Markdown
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# Spikey
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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.
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## Challenge Overview
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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).
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## Installation
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To install the necessary dependencies, create a virtual environment and install the requirements:
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```bash
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python3 -m venv env
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source env/bin/activate
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pip install -r requirements.txt
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```
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## Usage
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### Configuration
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The configuration for training and evaluation is specified in a YAML file. Below is an example configuration:
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```yaml
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name: Test
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preprocessing:
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use_delta_encoding: true # Whether to use delta encoding.
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predictor:
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type: lstm # Options: 'lstm', 'fixed_input_nn'
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input_size: 1 # Input size for the LSTM predictor.
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hidden_size: 128 # Hidden size for the LSTM or Fixed Input NN predictor.
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num_layers: 2 # Number of layers for the LSTM predictor.
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fixed_input_size: 10 # Input size for the Fixed Input NN predictor. Only used if type is 'fixed_input_nn'.
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training:
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epochs: 10 # Number of training epochs.
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batch_size: 32 # Batch size for training.
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learning_rate: 0.001 # Learning rate for the optimizer.
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eval_freq: 2 # Frequency of evaluation during training (in epochs).
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save_path: models # Directory to save the best model and encoder.
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num_points: 1000 # Number of data points to visualize.
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bitstream_encoding:
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type: arithmetic # Use arithmetic encoding.
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data:
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url: https://content.neuralink.com/compression-challenge/data.zip # URL to download the dataset.
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directory: data # Directory to extract and store the dataset.
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split_ratio: 0.8 # Ratio to split the data into train and test sets.
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```
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### Running the Code
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To train the model and compress/decompress WAV files, use the CLI provided:
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```bash
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python cli.py compress --config config.yaml --input_file path/to/input.wav --output_file path/to/output.bin
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python cli.py decompress --config config.yaml --input_file path/to/output.bin --output_file path/to/output.wav
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```
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### Training
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Requires Slate, which is not currently publicaly avaible. Install via (requires repo access)
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```bash
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pip install -e git+ssh://git@dominik-roth.eu/dodox/Slate.git#egg=slate
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```
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To train the model, run:
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```bash
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python main.py config.yaml Test
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```
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