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.
The Neuralink N1 implant generates approximately 200 Mbps of electrode data (1024 electrodes @ 20 kHz, 10-bit resolution) and can transmit data wirelessly at about 1 Mbps. This means a compression ratio of over 200x is required. The compression must run in real-time (<1ms)andconsumelowpower(<10mW,includingradio).
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 the first step, we analyze readings from the leads to construct an approximate 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 may be updated for thread movements but is not part of the regular compression/decompression process.
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 must be transmitted. We make use of the previously constructed topology to allow the predictive model's latent to represent the activity of brain regions based on the reading of the threads instead of just for threads themselves.
1.**Feature Extraction**: This module processes a given history of readings for a single thread and extracts relevant features (using mostly wavelet and Fourier transforms). Highly configurable, this module performs the heavy lifting of signal analysis, allowing shallow neural networks to handle the rest. (Full disclosure: I have no idea what half of the implemented wavelet transforms actually do. We just throw anything sensible at the problem and will narrow down later; making effective use of the fact that 'fuck around' and 'find out' are positively correlated.)
2.**Latent Projector**: This takes the feature vectors and projects them into a latent space. The latent projector can be configured as a fully connected network or an RNN (LSTM) with an arbitrary shape.
3.**[MiddleOut](https://www.youtube.com/watch?v=l49MHwooaVQ)**: For each lead, this module performs message passing according to the thread topology. Their latent representations along with their distance metrics are used to generate region latent representations. This is done by training a fully connected layer to map from (our_latent, their_latent, metric) -> region_latent and then averaging over all region_latent values to get the final representation.
4.**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. It can be configured to be an FCNN of arbitrary shape.
Expecting a 200x compression ratio is ludicrous, as it would mean transmitting only 1 bit per 20 data points. Given the high entropy of the readings, this is an absurd goal. Anyone who thinks lossless 200x compression is remotely feasible has a woefully inadequate grasp of information theory. Please, do yourself a favor and read Shannon’s paper.
Furthermore, there's no need for lossless compression. These readings feed into an ML model to extract intent, and any such encoder inherently reduces information content with each layer ('intelligence is the ability to disregard irrelevant information'). Instead, compression should be regarded as an integral part of the ML pipeline for intent extraction. It should be allowed to be lossy, with the key being to define the loss metric not by information loss in the input space, but rather in the latent space of the pipeline.
Let's see how far we can get with the approach presented here...
Why is the dataset provided not 10-bit if the readings are? They are all 16-bit. And the last 6 bits are not all zeros. We know they can't encode sensible information when the readings are only 10-bit, but we also can't just throw them away since they do contain something. We also observe that all possible values the data points can take on are separated by 64 or 63 (64 would make sense; 63 very much does not). (See `fucked_up_wavs.py`)
The provided eval.sh script is also somewhat flawed (as in: not aligned with what should be optimized for), since it counts the size of the compressor and decompressor as part of the transmitted data. Especially the decompressor part makes no sense. It also makes it impossible to compress data from multiple threads together, which is required for the free lunch we can get from topological reconstruction.
- Our flagship bitstream encoder builds an optimal Huffman tree assuming the deltas are binomially distributed. This should be updated when we know a more precise approximation of the delta distribution.
The icon used in this repository is a combination of the Pied Piper logo from the HBO show _Silicon Valley_ and the Neuralink logo. I do not hold any trademarks on either logo; they are owned by their respective entities.
## License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). For commercial use, please contact me at [mail@dominik-roth.eu](mailto:mail@dominik-roth.eu).
You can view the full text of the license [here](LICENSE).
