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CCR
2010
156views more  CCR 2010»
15 years 2 months ago
Evolvable network architectures: what can we learn from biology?
There is significant research interest recently to understand the evolution of the current Internet, as well as to design clean-slate Future Internet architectures. Clearly, even ...
Constantine Dovrolis, J. Todd Streelman
IEEECIT
2006
IEEE
15 years 8 months ago
Improving Resiliency of Network Topology with Enhanced Evolving Strategies
— Recent studies have shown that many real networks follow the power-law distribution of node degrees. Instead of random connectivity, however, power-law connectivity suffers fro...
Soo Kim, Heejo Lee, Wan Yeon Lee
ICANN
2007
Springer
15 years 8 months ago
Zero-Lag Long Range Synchronization of Neurons Is Enhanced by Dynamical Relaying
Abstract. How can two distant neural assemblies synchronize their firings at zero-lag even in the presence of non-negligible delays in the transfer of information between them? He...
Raul Vicente, Gordon Pipa, Ingo Fischer, Claudio R...
TNN
1998
92views more  TNN 1998»
15 years 1 months ago
Inductive inference from noisy examples using the hybrid finite state filter
—Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language,...
Marco Gori, Marco Maggini, Enrico Martinelli, Giov...
NN
1998
Springer
108views Neural Networks» more  NN 1998»
15 years 1 months ago
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NA...
Tsungnan Lin, Bill G. Horne, C. Lee Giles