  Understanding the coordinated activity underlying brain computations requires
large-scale, simultaneous recordings from distributed neuronal structures at a
cellular-level resolution. One major hurdle to design high-bandwidth,
high-precision, large-scale neural interfaces lies in the formidable data
streams that are generated by the recorder chip and need to be online
transferred to a remote computer. The data rates can require hundreds to
thousands of I/O pads on the recorder chip and power consumption on the order
of Watts for data streaming alone. We developed a deep learning-based
compression model to reduce the data rate of multichannel action potentials.
The proposed model is built upon a deep compressive autoencoder (CAE) with
discrete latent embeddings. The encoder is equipped with residual
transformations to extract representative features from spikes, which are
mapped into the latent embedding space and updated via vector quantization
(VQ). The decoder network reconstructs spike waveforms from the quantized
latent embeddings. Experimental results show that the proposed model
consistently outperforms conventional methods by achieving much higher
compression ratios (20-500x) and better or comparable reconstruction
accuracies. Testing results also indicate that CAE is robust against a diverse
range of imperfections, such as waveform variation and spike misalignment, and
has minor influence on spike sorting accuracy. Furthermore, we have estimated
the hardware cost and real-time performance of CAE and shown that it could
support thousands of recording channels simultaneously without excessive
power/heat dissipation. The proposed model can reduce the required data
transmission bandwidth in large-scale recording experiments and maintain good
signal qualities. The code of this work has been made available at
https://github.com/tong-wu-umn/spike-compression-autoencoder
