Visual attention using spiking neural maps

Roberto Vazquez 1, * Bernard Girau 2 Jean-Charles Quinton 2
* Auteur correspondant
1 CIC-IPN - Centro de Investigación en Computación
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Visual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noise or distracters. Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model.
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Communication dans un congrès
International Joint Conference on Neural Networks IJCNN 2011, Jul 2011, San José, United States. IEEE Computational Intelligence Society, 2011, The 2011 International Joint Conference on Neural Networks
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Dernière modification le : jeudi 11 janvier 2018 - 06:19:48
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Roberto Vazquez, Bernard Girau, Jean-Charles Quinton. Visual attention using spiking neural maps. International Joint Conference on Neural Networks IJCNN 2011, Jul 2011, San José, United States. IEEE Computational Intelligence Society, 2011, The 2011 International Joint Conference on Neural Networks. 〈inria-00603929〉

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