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Chaotic Phase Synchronization for Visual Selection

International Joint Conference on Neural Networks – IJCNN 2009. Chaotic Phase Synchronization for Visual Selection. Fabricio A. Breve¹ fabricio@icmc.usp.br Liang Zhao¹ zhao@icmc.usp.br Marcos G. Quiles¹ quiles@icmc.usp.br Elbert E. N. Macau² elbert@lac.inpe.br.

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Chaotic Phase Synchronization for Visual Selection

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  1. InternationalJointConferenceon Neural Networks – IJCNN 2009 Chaotic Phase Synchronization for Visual Selection Fabricio A. Breve¹fabricio@icmc.usp.br LiangZhao¹zhao@icmc.usp.br Marcos G. Quiles¹ quiles@icmc.usp.br Elbert E. N. Macau² elbert@lac.inpe.br ¹ Departmentof Computer Science, Institute of Mathematics and Computer Science, Universityof São Paulo, São Carlos-SP, Brazil ² NationalInstitute for SpaceResearch, São José dos Campos-SP, Brazil

  2. Outline • Visual Selection • Chaotic Phase Synchronization • Model Description • Computer Simulations • Artificial images • Real-world images • Conclusions

  3. Visual Selection [FRI01, KIM07, BUI06, NIE94, SHI07, TSO92, ITT01, CAR04] • Capacitydevelopedby living systems to select just relevant environmental information • Identifies the region of the visual input that will reach awareness level (focus of attention) while irrelevant information is suppressed

  4. ChaoticPhaseSynchronization as [PIK01, ROS96] Two oscillators are called phase synchronized if their phase difference is kept bounded while their amplitudes may be completely uncorrelated

  5. ChaoticPhaseSynchronization TwocoupledRössleroscillators: [ROS96, OSI97]

  6. ModelDescription Two dimensional network ofRösslerOscillators:

  7. ModelDescription • Oscillatorswhichcorresponds to pixels with: • highercontrast • Negativecouplingstrengthtends to zero • Theywillbesynchronized in phase • lowercontrast • Negativecouplingstrength is higher • Theywillrepeleachother. • After some time, onlytheoscillatorscorresponding to thesalientobjectwillremainwiththeirtrajectoriessynchronized in phasewhiletheotherobjectswillhavetrajectorieswithdifferentphases.

  8. Computer Simulations ARTIFICIAL IMAGES

  9. Artificial Imagewithhighcontrast

  10. Artificial Imagewithhighcontrast

  11. Artificial Imagewithlowcontrast

  12. Artificial Imagewithlowcontrast

  13. Artificial Imagewithlowcontrast

  14. ComputerSimulations Real-world images

  15. Real-world Image: Bird

  16. Real-world Image: Dog

  17. Real-world Image: Flower

  18. Conclusions • The proposed model can be applied to object selection • Chaotic Phase Synchronization • Used to discriminate the salient object from the visual input while keeping the non-salient, or less salient, objects unsynchronized • Main Advantages: • Robustness • Requires small coupling strength • Biological inspiration • Observed in nonidentical systems • Believed to be the key mechanism for neural integration in brain[VAR01]

  19. Acknowledgements • This work was supported by the State of São Paulo Research Foundation (FAPESP) and the Brazilian National Council of Technological and ScientificDevelopment (CNPq)

  20. References [FRI01] P. Fries, J. H. Reynolds, A. E. Rorie, and R. Desimone, “Modulation of oscillatory neuronal synchronization by selective visual attention,” Science, vol. 291, no. 5508, pp. 1560–1563, 2001. [KIM07] Y. J. Kim, M. Grabowecky, K. A. Paller, K. Muthu, and S. Suzuki, “Attention induces synchronization-based response gain in steady-state visual evokedpotentials,” NatureNeuroscience, vol. 10, no. 1, p.117–125, 2007. [BUI06] C. Buia and P. Tiesinga, “Attentional modulation of firing rate and synchrony in a model cortical network,” Journal of Computational Neuroscience, vol. 20, pp. 247–264, 2006. [NIE94] E. Niebur and C. Koch, “A model for neuronal implementation of selective visual attention based on temporal correlation among neurons,” Journal of Computational Neuroscience, vol. 1, pp. 141–158, 1994. [SHI07] F. Shic and B. Scassellati, “A behavioral analysis of computational models of visual attention,” International Journal of Computer Vision, vol. 73, no. 2, pp. 159–177, 2007. [TSO92] J. K. Tsotsos, “On the relative complexity of active vs. passive visual search,” International Journal of Computer Vision, vol. 7, pp. 127–141, 1992. [ITT01] L. Itti and C. Koch, “Computational modelling of visual attention,” Nature Reviews Neuroscience, vol. 2, pp. 194–203, 2001. [CAR04] L. Carota, G. Indiveri, and V. Dante, “A softwarehardwareselectiveattention system,” Neurocomputing, vol. 58-60, pp. 647–653, 2004. [PIK01] A. Pikovsky, M. Rosenblum, and J. Kurths, Synchronization: A universal concept in nonlinear sciences. Cambridge University Press, 2001. [ROS96] M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “Phase synchronization of chaotic oscillators,” Phisical Review Letters, vol. 76, no. 7, pp. 1804–1807, March 1996., [OSI97] G. V. Osipov, A. S. Pikovsky, M. G. Rosenblum, and J. Kurths, “Phasesynchronizationeffects in a latticeofnonidenticalr¨ossleroscillators,” Phys. Rev. E, vol. 55, no. 3, pp. 2353–2361, Mar 1997. [VAR01] F. Varela, J.-P. Lachaux, E. Rodriguez, and J. Martinerie, “Thebrainweb: Phasesynchronizationandlarge-scaleintegration,” NatureReviewsNeuroscience, vol. 2, pp. 229–239, April 2001.

  21. InternationalJointConferenceon Neural Networks – IJCNN 2009 Chaotic Phase Synchronization for Visual Selection Fabricio A. Breve¹fabricio@icmc.usp.br LiangZhao¹zhao@icmc.usp.br Marcos G. Quiles¹ quiles@icmc.usp.br Elbert E. N. Macau² elbert@lac.inpe.br ¹ Departmentof Computer Science, Institute of Mathematics and Computer Science, Universityof São Paulo, São Carlos-SP, Brazil ² NationalInstitute for SpaceResearch, São José dos Campos-SP, Brazil

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