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Please use this identifier to cite or link to this item: http://hdl.handle.net/10445/5288

Title: Selective Attention Improves Self-Organization of Cortical Maps with Multiple Inputs
Authors: Trappenberg, Thomas
Saito, Aya
Hartono, Pitoyo
Abstract: Models of self-organizing cortical maps have focused on demonstrations with single objects in the environment. Recently, the validity of a traditional biological model has been questioned for the case of multiple simultaneous input sources. Here we show that the standard model is able to self-organize with multiple inputs. However, we also show that the ability to self-organization can be enhanced considerably by including top-down attention as well as some noise. The model is also used to simulate the development of tuning curves.
Research Achievement Classification: 国際会議/International Conference
Type: Presentation
Peer Review: あり/yes
Solo/Joint Author(s): 共著/joint
Published journal or presented
academic conference: 
Proc. International Joint Conference on Neural Networks (IJCNN 2010)(accepted)
Date: 2010
Publisher: IEEE
Appears in Collections:Pitoyo, Hartono

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