# Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

arXiv: https://arxiv.org/abs/2109.08945 · HTML: https://mlsanity.com/paper/2109.08945/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [Complex Deep Learning Models for Denoising of Human Heart ECG signals](https://mlsanity.com/paper/1908.10417/) (2019)

## Built on by (most cited first, 2 of 2)

- [Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting](https://mlsanity.com/paper/2506.24041/) (2025)
- [A Fully Convolutional Approach to Denoising 2D Correlation Spectra](https://mlsanity.com/paper/2605.29975/) (2026)

## Abstract (arXiv)

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.