Perspective on applications of a stochastic spiking neuron model to neural network modeling (2016)
- Autores:
- Autores USP: SILVA FILHO, ANTONIO CARLOS ROQUE DA - FFCLRP ; KINOUCHI FILHO, OSAME - FFCLRP ; ABADI, MIGUEL NATALIO - IME
- Unidades: FFCLRP; IME
- Assuntos: NEUROCIÊNCIAS; SINAPSE; MODELOS PARA PROCESSOS ESTOCÁSTICOS
- Idioma: Inglês
- Imprenta:
- Fonte:
- Título do periódico: Abstracts
- Nome do evento: Neuromat Workshop on High-Performance Computing, Stochastic Modeling and Databases in Neuroscience
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ABNT
ROQUE, Antônio Carlos et al. Perspective on applications of a stochastic spiking neuron model to neural network modeling. 2016, Anais.. São Paulo: USP, 2016. Disponível em: https://pt.slideshare.net/neuromathematics?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview. Acesso em: 19 set. 2024. -
APA
Roque, A. C., Brochini, L., Costa, A., Cordeiro, V., Shimoura, R., Abadi, M. N., et al. (2016). Perspective on applications of a stochastic spiking neuron model to neural network modeling. In Abstracts. São Paulo: USP. Recuperado de https://pt.slideshare.net/neuromathematics?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview -
NLM
Roque AC, Brochini L, Costa A, Cordeiro V, Shimoura R, Abadi MN, Kinouchi O, Stolfi J. Perspective on applications of a stochastic spiking neuron model to neural network modeling [Internet]. Abstracts. 2016 ;[citado 2024 set. 19 ] Available from: https://pt.slideshare.net/neuromathematics?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview -
Vancouver
Roque AC, Brochini L, Costa A, Cordeiro V, Shimoura R, Abadi MN, Kinouchi O, Stolfi J. Perspective on applications of a stochastic spiking neuron model to neural network modeling [Internet]. Abstracts. 2016 ;[citado 2024 set. 19 ] Available from: https://pt.slideshare.net/neuromathematics?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview - Dynamical neuronal gains produce self-organized criticality in stochastic spiking neural networks
- Phase transitions and self-organized criticality in networks of stochastic spiking neurons
- 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
- Explaining the hovering stochastic oscillations in self-organized quasi-critical systems
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