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Filtros : "MODELOS PARA PROCESSOS ESTOCÁSTICOS" "REDES NEURAIS" Removidos: "Web of Science" "Teses e dissertações" Limpar

Filtros

USP affiliated authors
- silva filho, antonio carlos roque da (~2)
- kinouchi filho, osame (~1)
Date published
- 2018 (~1)
- 2016 (~1)
Subjects
- modelos para processos estocásticos (~2)
- redes neurais (~2)
- neurociências (~1)
- rede nervosa (~1)
- redes complexas (~1)
Language
- inglês (~2)
Source title
- abstracts (~1)
- frontiers in computational neuroscience (~1)
Publisher
- usp (~1)
Conference titles
- neuromat workshop on high-performance computing, stochastic modeling and databases in neuroscience (~1)
Countries/Territories
- brasil (~1)
- suiça (~1)
Grupo de pesquisa
Funding Agencies
- financiado pela dfg (~1)
- financiado pela fapesp (~1)
- financiado pelo bmbf (~1)
- financiado pelo cnpq (~1)
Indexado em
- indexado no: biological abstracts (1)
- indexado no: embase (1)
- indexado no: pubmed (1)
- indexado no: science citation index expanded (1)
- indexado no: scopus (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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] Available from: https://doi.org/10.3389/fncom.2018.00009
Trabalho de evento-resumo
Applications of a stochastic spiking neuron model to neural network modeling (2016)
Roque, Antônio Carlos; Brochini, Ludmila; Costa, Ariadne; Cordeiro, Vinícius; Shimoura, Renan; Kamiji, Nilton; Kinouchi, Osame; Stolfi, Jorge; Brasil
Source: Abstracts. Conference titles: Neuromat Workshop on High-Performance Computing, Stochastic Modeling and Databases in Neuroscience. Unidade: FFCLRP
Subjects: NEUROCIÊNCIAS, REDES NEURAIS, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROQUE, Antônio Carlos et al. Applications of a stochastic spiking neuron model to neural network modeling. 2016, Anais.. São Paulo: USP, 2016. . Acesso em: 07 nov. 2024.
APA
Roque, A. C., Brochini, L., Costa, A., Cordeiro, V., Shimoura, R., Kamiji, N., et al. (2016). Applications of a stochastic spiking neuron model to neural network modeling. In Abstracts. São Paulo: USP.
NLM
Roque AC, Brochini L, Costa A, Cordeiro V, Shimoura R, Kamiji N, Kinouchi O, Stolfi J, Brasil. Applications of a stochastic spiking neuron model to neural network modeling. Abstracts. 2016 ;[citado 2024 nov. 07 ]
Vancouver
Roque AC, Brochini L, Costa A, Cordeiro V, Shimoura R, Kamiji N, Kinouchi O, Stolfi J, Brasil. Applications of a stochastic spiking neuron model to neural network modeling. Abstracts. 2016 ;[citado 2024 nov. 07 ]

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