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
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 12 fev. 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 fev. 12 ] 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 fev. 12 ] 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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Informações sobre o DOI: 10.1162/neco_a_01277 (Fonte: oaDOI API)
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