Comparison of different spike train synchrony measures regarding their robustness to erroneous data from bicuculline-induced epileptiform activity (2020)
- Authors:
- USP affiliated authors: COSTA, LUCIANO DA FONTOURA - IFSC ; RODRIGUES, FRANCISCO APARECIDO - ICMC ; PERON, THOMAS KAUÊ DAL'MASO - ICMC
- Unidades: IFSC; ICMC
- DOI: 10.1162/neco_a_01277
- Subjects: CÉREBRO; REDES NEURAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Neural Computation
- ISSN: 1530-888X
- Volume/Número/Paginação/Ano: v. 32, n. 5, p. 887-911, May 2020
- Status:
- Artigo possui acesso gratuito no site do editor (Bronze Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
CIBA, Manuel et al. Comparison of different spike train synchrony measures regarding their robustness to erroneous data from bicuculline-induced epileptiform activity. Neural Computation, v. 32, n. 5, p. 887-911, 2020Tradução . . Disponível em: https://doi.org/10.1162/neco_a_01277. Acesso em: 30 mar. 2026. -
APA
Ciba, M., Bestel, R., Nick, C., Arruda, G. F. de, Peron, T., Comin, C. H., et al. (2020). Comparison of different spike train synchrony measures regarding their robustness to erroneous data from bicuculline-induced epileptiform activity. Neural Computation, 32( 5), 887-911. doi:10.1162/neco_a_01277 -
NLM
Ciba M, Bestel R, Nick C, Arruda GF de, Peron T, Comin CH, Costa L da F, Rodrigues FA, Thielemann C. Comparison of different spike train synchrony measures regarding their robustness to erroneous data from bicuculline-induced epileptiform activity [Internet]. Neural Computation. 2020 ; 32( 5): 887-911.[citado 2026 mar. 30 ] Available from: https://doi.org/10.1162/neco_a_01277 -
Vancouver
Ciba M, Bestel R, Nick C, Arruda GF de, Peron T, Comin CH, Costa L da F, Rodrigues FA, Thielemann C. Comparison of different spike train synchrony measures regarding their robustness to erroneous data from bicuculline-induced epileptiform activity [Internet]. Neural Computation. 2020 ; 32( 5): 887-911.[citado 2026 mar. 30 ] Available from: https://doi.org/10.1162/neco_a_01277 - A machine learning approach to predicting dynamical observables from network structure
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