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12

IJCNN
2006
IEEE

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Learning using Dynamical Regime Identification and Synchronization

13 years 10 months ago

Learning using Dynamical Regime Identification and Synchronization

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—This study proposes to generalize Hebbian learning by identifying and synchronizing the dynamical regimes of individual nodes in a recurrent network. The connection weights are updated according to the closeness in the observed local dynamical regimes. Demonstration of the viability of this method is provided on spiking recurrent neural networks. Experiments are made with both artificial and real continuous data, using a frequency population coding.

Nicolas Brodu

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Dynamical Regimes | IJCNN 2006 | Local Dynamical Regimes | Recurrent Neural Networks |

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Added	11 Jun 2010
Updated	11 Jun 2010
Type	Conference
Year	2006
Where	IJCNN
Authors	Nicolas Brodu

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