Author

A. C. d. S. Silva

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Animal Behavior & Cognition· 1 Bioengineering· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Assay:electrophysiology· 3 Organism:chicken / avian· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:plant (other)· 1

Frequent Co-Authors

E. J. X. Costa· 2 shared H. L. A. Minano· 2 shared A. I. C. Arce· 1 shared A. R. Silva· 1 shared E. Costa· 1 shared G. V. Atzingen· 1 shared G. V. v. Atzingen· 1 shared N. F. Ortega· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBioengineering

E. J. X. Costa

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Animal Behavior & CognitionbioRxivPreprintNov 22, 2021

Improving behavior monitoring of free moving dairy cows using noninvasive wireless EEG approach and digital signal processing techniques.

A. C. d. S. Silva, A. I. C. Arce, H. L. A. Minano…·E. Costa

This paper develops a noninvasive wireless EEG method to monitor brain activity in freely moving dairy cows using signal processing techniques and evaluates its effectiveness compared to restrained conditions.

DOI 10.1101/2021.11.22.469585

chicken / avianelectrophysiology

𝕏 PostBluesky

BioengineeringbioRxivPreprintOct 25, 2021

Quantifying the effect of caloric and non-caloric sweeteners in the brain response using EEG and convolutional neural network

G. V. v. Atzingen, H. L. A. Minano, A. R. Silva…·A. C. d. S. Silva

This study uses EEG and a convolutional neural network to distinguish brain responses to different sweeteners, achieving 82.3% accuracy in identifying sucrose, sucralose, and aspartame.

DOI 10.1101/2021.10.25.465723

humanelectrophysiology

𝕏 PostBluesky

Plant BiologybioRxivNov 10, 2020

Bioelectrical pattern discrimination of Miconia plants by spectral analysis and machine learning

V. M. M. Gimenez, P. M. Pauleti, A. C. d. S. Silva, E. J. X. Costa

The paper characterizes bioelectrical signals of two Miconia species using spectral analysis and machine learning, finding distinct frequency patterns between species.

DOI 10.1007/s40626-021-00214-0

plant (other)electrophysiology

𝕏 PostBluesky