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GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
14 years 2 months ago
Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks
In this work MagnetoEncephaloGram (MEG) recordings of epileptic patients were analyzed using a hybrid neural networks training algorithm. This algorithm combines genetic algorithm...
Adam V. Adamopoulos, Efstratios F. Georgopoulos, S...
FLAIRS
2004
13 years 11 months ago
Simulating Biological Motion Perception Using a Recurrent Neural Network
People have the ability to perceive biological motion under conditions of severely limited visual information. If the information is in the form of a point-light motion sequence o...
Roxanne L. Canosa
WCE
2007
13 years 11 months ago
E-learning System Based on Neural Networks
—Although the current E-Learning systems have many merits, many of them only treat advanced information technology as simple communication tools, and release some learning conten...
Linfeng Zhang, Fei Yu, Yue Shen, Guiping Liao, Ken...
BIOSYSTEMS
2007
82views more  BIOSYSTEMS 2007»
13 years 9 months ago
Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks
Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node ...
Hywel T. P. Williams, Jason Noble
KES
2008
Springer
13 years 9 months ago
On the use of spiking neural network for EEG classification
This paper presents a new classification technique of continuous EEG recordings, based on a network of spiking neurons. Human EEG signals published on the BCI Competition website w...
Piyush Goel, Honghai Liu, David J. Brown, Avijit D...