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Artigo de periodico
Influence maximization by rumor spreading on correlated networks through community identification (2020)
Vega-Oliveros, Didier Augusto ; Costa, Luciano da Fontoura ; Rodrigues, Francisco Aparecido
Source: Communications in Nonlinear Science and Numerical Simulation. Unidades: IFSC, ICMC, FFCLRP
Subjects: REDES COMPLEXAS, ESPALHAMENTO, BOATO, DIFUSÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VEGA-OLIVEROS, Didier Augusto e COSTA, Luciano da Fontoura e RODRIGUES, Francisco Aparecido. Influence maximization by rumor spreading on correlated networks through community identification. Communications in Nonlinear Science and Numerical Simulation, v. 83, p. 105094-1-105094-13, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2019.105094. Acesso em: 24 abr. 2024.
APA
Vega-Oliveros, D. A., Costa, L. da F., & Rodrigues, F. A. (2020). Influence maximization by rumor spreading on correlated networks through community identification. Communications in Nonlinear Science and Numerical Simulation, 83, 105094-1-105094-13. doi:10.1016/j.cnsns.2019.105094
NLM
Vega-Oliveros DA, Costa L da F, Rodrigues FA. Influence maximization by rumor spreading on correlated networks through community identification [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2020 ; 83 105094-1-105094-13.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1016/j.cnsns.2019.105094
Vancouver
Vega-Oliveros DA, Costa L da F, Rodrigues FA. Influence maximization by rumor spreading on correlated networks through community identification [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2020 ; 83 105094-1-105094-13.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1016/j.cnsns.2019.105094
Artigo de periodico
Temporal network pattern identification by community modelling (2020)
Gao, Xubo; Zheng, Qiusheng; Vega-Oliveros, Didier Augusto ; Anghinoni, Leandro ; Liang, Zhao
Source: Scientific Reports. Unidades: FFCLRP, ICMC
Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GAO, Xubo et al. Temporal network pattern identification by community modelling. Scientific Reports, v. 10, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-57123-1. Acesso em: 24 abr. 2024.
APA
Gao, X., Zheng, Q., Vega-Oliveros, D. A., Anghinoni, L., & Liang, Z. (2020). Temporal network pattern identification by community modelling. Scientific Reports, 10, 1-12. doi:10.1038/s41598-019-57123-1
NLM
Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1038/s41598-019-57123-1
Vancouver
Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1038/s41598-019-57123-1
Artigo de periodico
Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks (2018)
Pena, Rodrigo Felipe de Oliveira; Vellmer, Sebastian; Bernardi, Davide; Roque, Antônio Carlos; Lindner, Benjamin
Source: Frontiers in Computational Neuroscience. Unidade: FFCLRP
Subjects: REDES COMPLEXAS, REDE NERVOSA, REDES NEURAIS, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PENA, Rodrigo Felipe de Oliveira et al. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks. Frontiers in Computational Neuroscience, v. 12, 2018Tradução . . Disponível em: https://doi.org/10.3389/fncom.2018.00009. Acesso em: 24 abr. 2024.
APA
Pena, R. F. de O., Vellmer, S., Bernardi, D., Roque, A. C., & Lindner, B. (2018). Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks. Frontiers in Computational Neuroscience, 12. doi:10.3389/fncom.2018.00009
NLM
Pena RF de O, Vellmer S, Bernardi D, Roque AC, Lindner B. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks [Internet]. Frontiers in Computational Neuroscience. 2018 ; 12[citado 2024 abr. 24 ] Available from: https://doi.org/10.3389/fncom.2018.00009
Vancouver
Pena RF de O, Vellmer S, Bernardi D, Roque AC, Lindner B. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks [Internet]. Frontiers in Computational Neuroscience. 2018 ; 12[citado 2024 abr. 24 ] Available from: https://doi.org/10.3389/fncom.2018.00009
Trabalho de evento-resumo
Redes neurais: modelagem e aspectos computacionais (2018)
Roque, Antônio Carlos
Source: Palestra. Conference titles: Semana Nacional do Cérebro. Unidade: FFCLRP
Subjects: REDES NEURAIS, NEURÔNIOS, SENSORES BIOMÉDICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROQUE, Antônio Carlos. Redes neurais: modelagem e aspectos computacionais. 2018, Anais.. Ribeirão Preto: SBNeC, 2018. Disponível em: https://repositorio.usp.br/directbitstream/4aa79b0c-18ba-41f2-a931-22d34e1ce0f8/002966759.pdf. Acesso em: 24 abr. 2024.
APA
Roque, A. C. (2018). Redes neurais: modelagem e aspectos computacionais. In Palestra. Ribeirão Preto: SBNeC. Recuperado de https://repositorio.usp.br/directbitstream/4aa79b0c-18ba-41f2-a931-22d34e1ce0f8/002966759.pdf
NLM
Roque AC. Redes neurais: modelagem e aspectos computacionais [Internet]. Palestra. 2018 ;[citado 2024 abr. 24 ] Available from: https://repositorio.usp.br/directbitstream/4aa79b0c-18ba-41f2-a931-22d34e1ce0f8/002966759.pdf
Vancouver
Roque AC. Redes neurais: modelagem e aspectos computacionais [Internet]. Palestra. 2018 ;[citado 2024 abr. 24 ] Available from: https://repositorio.usp.br/directbitstream/4aa79b0c-18ba-41f2-a931-22d34e1ce0f8/002966759.pdf
Trabalho de evento-resumo
Mathematical models in neuroscience: from neurons to networks (2018)
Roque, Antônio Carlos
Source: Palestra. Conference titles: Palestra no Laboratório de Sistemas Neurais - SisNe. Unidade: FFCLRP
Subjects: REDES NEURAIS, NEUROCIÊNCIAS, COMPORTAMENTO (MODELOS MATEMÁTICOS)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROQUE, Antônio Carlos. Mathematical models in neuroscience: from neurons to networks. 2018, Anais.. Ribeirão Preto: SisNe/USP, 2018. Disponível em: https://repositorio.usp.br/directbitstream/de9edbd3-bdc3-44d3-a941-be922dfef31f/002967299.pdf. Acesso em: 24 abr. 2024.
APA
Roque, A. C. (2018). Mathematical models in neuroscience: from neurons to networks. In Palestra. Ribeirão Preto: SisNe/USP. Recuperado de https://repositorio.usp.br/directbitstream/de9edbd3-bdc3-44d3-a941-be922dfef31f/002967299.pdf
NLM
Roque AC. Mathematical models in neuroscience: from neurons to networks [Internet]. Palestra. 2018 ;[citado 2024 abr. 24 ] Available from: https://repositorio.usp.br/directbitstream/de9edbd3-bdc3-44d3-a941-be922dfef31f/002967299.pdf
Vancouver
Roque AC. Mathematical models in neuroscience: from neurons to networks [Internet]. Palestra. 2018 ;[citado 2024 abr. 24 ] Available from: https://repositorio.usp.br/directbitstream/de9edbd3-bdc3-44d3-a941-be922dfef31f/002967299.pdf
Trabalho de evento-resumo periodico
Determination of the spike‑train power spectrum statistics in modular networks with mixtures of diferent excitatory and inhibitory populations (2017)
Pena, Rodrigo Felipe de Oliveira; Bernardi, Davide; Roque, Antônio Carlos; Lindner, Benjamin
Source: BMC Neuroscience. Conference titles: Annual Computational Neuroscience Meeting (CNS). Unidade: FFCLRP
Subjects: REDES NEURAIS, NEUROCIÊNCIAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PENA, Rodrigo Felipe de Oliveira et al. Determination of the spike‑train power spectrum statistics in modular networks with mixtures of diferent excitatory and inhibitory populations. BMC Neuroscience. London: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://bmcneurosci.biomedcentral.com/track/pdf/10.1186/s12868-017-0371-2. Acesso em: 24 abr. 2024. , 2017
APA
Pena, R. F. de O., Bernardi, D., Roque, A. C., & Lindner, B. (2017). Determination of the spike‑train power spectrum statistics in modular networks with mixtures of diferent excitatory and inhibitory populations. BMC Neuroscience. London: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Recuperado de https://bmcneurosci.biomedcentral.com/track/pdf/10.1186/s12868-017-0371-2
NLM
Pena RF de O, Bernardi D, Roque AC, Lindner B. Determination of the spike‑train power spectrum statistics in modular networks with mixtures of diferent excitatory and inhibitory populations [Internet]. BMC Neuroscience. 2017 ; 18 59.[citado 2024 abr. 24 ] Available from: https://bmcneurosci.biomedcentral.com/track/pdf/10.1186/s12868-017-0371-2
Vancouver
Pena RF de O, Bernardi D, Roque AC, Lindner B. Determination of the spike‑train power spectrum statistics in modular networks with mixtures of diferent excitatory and inhibitory populations [Internet]. BMC Neuroscience. 2017 ; 18 59.[citado 2024 abr. 24 ] Available from: https://bmcneurosci.biomedcentral.com/track/pdf/10.1186/s12868-017-0371-2

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