Phase transitions and self-organized criticality in networks of stochastic spiking neurons (2016)
- Authors:
- USP affiliated authors: ABADI, MIGUEL NATALIO - IME ; SILVA FILHO, ANTONIO CARLOS ROQUE DA - FFCLRP ; KINOUCHI FILHO, OSAME - FFCLRP
- Unidades: IME; FFCLRP
- DOI: 10.1038/srep35831
- Subjects: NEUROCIÊNCIAS; COMPORTAMENTO ANIMAL; SINAPSE; CÉLULAS DENDRÍTICAS; ESTATÍSTICA DE PROCESSOS ESTOCÁSTICOS
- Language: Inglês
- Imprenta:
- Source:
- Título: Scientific Reports
- ISSN: 2045-2322
- Volume/Número/Paginação/Ano: v. 6, art. 35831, 2016
- 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. Phase transitions and self-organized criticality in networks of stochastic spiking neurons. Scientific Reports, v. 6, 2016Tradução . . Disponível em: https://doi.org/10.1038/srep35831. Acesso em: 29 dez. 2025. -
APA
Brochini, L., Costa, A. de A., Abadi, M. N., Roque, A. C., Stolfi, J., & Kinouchi, O. (2016). Phase transitions and self-organized criticality in networks of stochastic spiking neurons. Scientific Reports, 6. doi:10.1038/srep35831 -
NLM
Brochini L, Costa A de A, Abadi MN, Roque AC, Stolfi J, Kinouchi O. Phase transitions and self-organized criticality in networks of stochastic spiking neurons [Internet]. Scientific Reports. 2016 ; 6[citado 2025 dez. 29 ] Available from: https://doi.org/10.1038/srep35831 -
Vancouver
Brochini L, Costa A de A, Abadi MN, Roque AC, Stolfi J, Kinouchi O. Phase transitions and self-organized criticality in networks of stochastic spiking neurons [Internet]. Scientific Reports. 2016 ; 6[citado 2025 dez. 29 ] Available from: https://doi.org/10.1038/srep35831 - Perspective on applications of a stochastic spiking neuron model to neural network modeling
- Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks
- A stochastic version of the Potjans-Diesmann cortical column model
- Response of electrically coupled Hodgkin-dimensional lattice
- Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks
- Applications of a stochastic spiking neuron model to neural network modeling
- Signal compression in the sensory periphery
- The shortest-path random variable
- Latin American School on Computational Neurosciene (LASCON), 3
- Clustering indices and decay of correlations in non-Markovian models
Informações sobre o DOI: 10.1038/srep35831 (Fonte: oaDOI API)
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