Abstract
In this paper, we make some analysis on the FitzHugh-Nagumo model and improve it to build a neural network, and the network is used to implement visual selection and attention shifting. Each group of neurons representing one object of a visual input is synchronized; different groups of neurons representing different objects of a visual input are desynchronized. Cooperation and competition mechanism is also introduced to accelerate oscillating frequency of the salient object as well as to slow down other objects, which result in the most salient object jumping to a high frequency oscillation, while all other objects being silent. The object corresponding to high frequency oscillation is selected, then the selected object is inhibited and other neurons continue to oscillate to select the next salient object.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, D.L.: Object selection based on oscillatory correlation. Neural Networks 12, 579–592 (1999)
Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)
Campbell, S.R., Wang, D.L.: Synchronization and desynchronization in a network of locally coupled wilson-cowan oscillators. IEEE Transactions on Neural Networks 7, 541–554 (1996)
Navalpakkam, V., Itti, L.: An Integrated Model of Top-down and Bottom-up Attention for Optimizing Detection Speed. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)
Deco, G.: A Neurodynamical Model of Visual Attention: Feedback Enhancement of Spatial Resolution in a Hierarchical System. Journal of Computational Neuroscience 10, 231–253 (2001)
Heinke, D., Humphreys, G.W.: SAIM: A Model of Visual Attention and Neglect. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 913–918. Springer, Heidelberg (1997)
Heinke, D.: Selective Attention for Identification Model: Simulating visual neglect. Computer Vision and Image Understanding 100, 172–197 (2005)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2, 194–203 (2001)
Zhao, L., Breve, F.A.: Visual Selection and Shifting Mechanisms Based on a Network of Chaotic Wilson-Cowan Oscillators. In: Third International Conference on Natural Computation (2007)
Zhao, L., Macau, E.E.N., Omar, N.: Scene segmentation of the chaotic oscillator Network. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 10(7), 1697–1708 (2000)
Quiles, M.G., Wang, D.L., Zhao, L., Romero, R.A.F., Huang, D.: An Oscillatory Correlation Model of Object-based Attention. In: Proc. IJCNN (2009)
Zhao, L.: A Dynamically Coupled Chaotic Oscillatory Correlation Network. In: Proc. VI Brazilian Symposium on Neural Networks (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Qiao, Y., Duan, L., Fang, F., Miao, J., Ma, B. (2010). Visual Selection and Attention Shifting Based on FitzHugh-Nagumo Equations. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)