Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks (2016)
- Authors:
- USP affiliated authors: ABADI, MIGUEL NATALIO - IME ; SILVA FILHO, ANTONIO CARLOS ROQUE DA - FFCLRP ; KINOUCHI FILHO, OSAME - FFCLRP
- Unidades: IME; FFCLRP
- DOI: 10.3389/conf.fninf.2016.20.00059
- Subjects: PROCESSOS ESTOCÁSTICOS; REDES NEURAIS; NEUROCIÊNCIAS
- Language: Inglês
- Imprenta:
- Publisher: Imperial College London
- Publisher place: London
- Date published: 2016
- Source:
- Título do periódico: Abstract
- Conference titles: Palestra no Centre of Complexity Science - Imperial College London
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
BROCHINI, Ludmila et al. Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks. 2016, Anais.. London: Imperial College London, 2016. Disponível em: https://doi.org/10.3389/conf.fninf.2016.20.00059. Acesso em: 19 abr. 2024. -
APA
Brochini, L., Costa, A. de A., Abadi, M. N., Roque, A. C., Stolfi, J., & Kinouchi, O. (2016). Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks. In Abstract. London: Imperial College London. doi:10.3389/conf.fninf.2016.20.00059 -
NLM
Brochini L, Costa A de A, Abadi MN, Roque AC, Stolfi J, Kinouchi O. Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks [Internet]. Abstract. 2016 ;[citado 2024 abr. 19 ] Available from: https://doi.org/10.3389/conf.fninf.2016.20.00059 -
Vancouver
Brochini L, Costa A de A, Abadi MN, Roque AC, Stolfi J, Kinouchi O. Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks [Internet]. Abstract. 2016 ;[citado 2024 abr. 19 ] Available from: https://doi.org/10.3389/conf.fninf.2016.20.00059 - Perspective on applications of a stochastic spiking neuron model to neural network modeling
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Informações sobre o DOI: 10.3389/conf.fninf.2016.20.00059 (Fonte: oaDOI API)
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