  Extracellular recordings are severely contaminated by a considerable amount
of noise sources, rendering the denoising process an extremely challenging task
that should be tackled for efficient spike sorting. To this end, we propose an
end-to-end deep learning approach to the problem, utilizing a Fully
Convolutional Denoising Autoencoder, which learns to produce a clean neuronal
activity signal from a noisy multichannel input. The experimental results on
simulated data show that our proposed method can improve significantly the
quality of noise-corrupted neural signals, outperforming widely-used wavelet
denoising techniques.

Neurons and Cognition;Machine Learning;Signal Processing