Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Removing Noise from Extracellular Neural Recordings Using Fully
  Convolutional Denoising Autoencoders   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.

21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex