Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks (2018)
Fonte: Frontiers in Computational Neuroscience. Unidade: FFCLRP
Assuntos: 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: 19 nov. 2025.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.2018.00009NLM
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 2025 nov. 19 ] Available from: https://doi.org/10.3389/fncom.2018.00009Vancouver
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 2025 nov. 19 ] Available from: https://doi.org/10.3389/fncom.2018.00009
