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ICANN
2010
Springer

Using Reinforcement Learning to Guide the Development of Self-organised Feature Maps for Visual Orienting

9 years 3 months ago
Using Reinforcement Learning to Guide the Development of Self-organised Feature Maps for Visual Orienting
We present a biologically inspired neural network model of visual orienting (using saccadic eye movements) in which targets are preferentially selected according to their reward value. Internal representations of visual features that guide saccades are developed in a self-organised map whose plasticity is modulated under reward. In this way, only those features relevant for acquiring rewarding targets are generated. As well as guiding the formation of feature representations, rewarding stimuli are stored in a working memory and bias future saccade generation. In addition, a reward prediction error is used to initiate retraining of the self-organised map to generate more efficient representations of the features when necessary.
Kevin Brohan, Kevin N. Gurney, Piotr Dudek
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICANN
Authors Kevin Brohan, Kevin N. Gurney, Piotr Dudek
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