International Conference on Nonlinear Science and Complexity - NSC, 6 (2016)
- Authors:
- USP affiliated authors: LIANG, ZHAO - FFCLRP ; SILVA FILHO, ANTONIO CARLOS ROQUE DA - FFCLRP ; CALDAS, IBERE LUIZ - IF ; EISENCRAFT, MARCIO - EP ; TUFAILE, ALBERTO - EACH ; RODRIGUES, FRANCISCO APARECIDO - ICMC ; PIQUEIRA, JOSÉ ROBERTO CASTILHO - EP ; ROMERO, ROSELI APARECIDA FRANCELIN - ICMC
- Unidades: FFCLRP; IF; EP; EACH; ICMC
- Subjects: SISTEMAS NÃO LINEARES; SISTEMAS DINÂMICOS
- Language: Inglês
- Imprenta:
- Publisher: INPE
- Publisher place: São José dos Campos
- Date published: 2016
-
ABNT
International Conference on Nonlinear Science and Complexity - NSC, 6. . São José dos Campos: INPE. . Acesso em: 04 mar. 2026. , 2016 -
APA
International Conference on Nonlinear Science and Complexity - NSC, 6. (2016). International Conference on Nonlinear Science and Complexity - NSC, 6. São José dos Campos: INPE. -
NLM
International Conference on Nonlinear Science and Complexity - NSC, 6. 2016 ;[citado 2026 mar. 04 ] -
Vancouver
International Conference on Nonlinear Science and Complexity - NSC, 6. 2016 ;[citado 2026 mar. 04 ] - Shannon entropy applied to the analysis of tonotopic reorganization in a computational model of classical conditioning
- Using information theory for the analysis of cortical reorganization in a realistic computational model of the somatosensory system
- Shannon's entropy applied to the analysis of tonotopic reorganization in a computational model of classical conditioning
- Shannon's entropy applied to the analysis of tonotopic reorganization in a model of classical conditioning
- Using information theory for the analysis of cortical reorganization in a realistic computational model of the somatosensory system
- Identifying abnormal nodes in protein-protein interaction networks
- Selecting salient objects in real scenes: an oscillatory correlation model
- Model of top-down/bottom-up visual attention for location of salient objects in specific domains
- An object-based visual selection framework
- A pulse-coupled neural network as a simplified bottom-up attention model
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