Single graded-response neuron model with recurrent excitation: distributed delay (1996)
- Authors:
- USP affiliated authors: MALTA, CORACI PEREIRA - IF ; RAGAZZO, CLODOALDO GROTTA - IME
- Unidades: IF; IME
- Assunto: FÍSICA MATEMÁTICA
- Language: Inglês
- Imprenta:
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ABNT
PAKDAMAN, K et al. Single graded-response neuron model with recurrent excitation: distributed delay. . São Paulo: IfUSP. Disponível em: http://publica-sbi.if.usp.br/PDFs/pd1223.pdf. Acesso em: 29 jan. 2026. , 1996 -
APA
Pakdaman, K., Malta, C. P., Ragazzo, C. G., & Vibert, J. F. (1996). Single graded-response neuron model with recurrent excitation: distributed delay. São Paulo: IfUSP. Recuperado de http://publica-sbi.if.usp.br/PDFs/pd1223.pdf -
NLM
Pakdaman K, Malta CP, Ragazzo CG, Vibert JF. Single graded-response neuron model with recurrent excitation: distributed delay [Internet]. 1996 ;[citado 2026 jan. 29 ] Available from: http://publica-sbi.if.usp.br/PDFs/pd1223.pdf -
Vancouver
Pakdaman K, Malta CP, Ragazzo CG, Vibert JF. Single graded-response neuron model with recurrent excitation: distributed delay [Internet]. 1996 ;[citado 2026 jan. 29 ] Available from: http://publica-sbi.if.usp.br/PDFs/pd1223.pdf - Effect of delay on the boundary of the basin of attraction in a system of two neurons
- "Asymptotic behavior of irreducible excitatory networks of analog graded-response neurons
- Bifurcation structure of scalar differential delayed equations
- Singularity structure of the hopf bifurcation surface of a differential equation with two delays
- Bifurcation structure of scalar differential delayed equations
- Effect of delay on the boundary of the basin of attraction in a self-excited single graded-response neuron
- Transition layer equations for positive-feedback delayed equations
- Oscillations in continuous-time ring neural network models with and without delay
- Asymptotic behavior of irreducible excitatory networks of graded-response neurons
- Non-existence of superexponential solutions in a system of delay differential equations modeling a two-neuron network
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