Abstract
Based on the information processing functionalities of spiking neurons, a hierarchical spiking neural network model is proposed to simulate visual attention. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the network are detailed in this paper. Simulation results show that the network is able to perform visual attention to extract objects based on specific image features. Using extraction of horizontal and vertical lines, a demonstration shows how the network can detect a house in a visual image. Using this visual attention principle, many other objects can be extracted by analogy.
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Place of Publication | Berlin Heidelberg |
| Publisher | Springer |
| Pages | 26-31 |
| Number of pages | 6 |
| Volume | 6840 |
| ISBN (Print) | 978-3-642-24552-7 |
| Publication status | Published - 4 Oct 2011 |
| Event | International Conference on Interlligent Computing (ICIC 2011) - Duration: 4 Oct 2011 → … |
Conference
| Conference | International Conference on Interlligent Computing (ICIC 2011) |
|---|---|
| Period | 4/10/11 → … |
Keywords
- Visual attention
- spiking neural network
- receptive field
- visual system.
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Cite this
Wu, Q., McGinnity, TM., Maguire, LP., Cai, R., & Chen, M. (2011). Simulation of Visual Attention using Hierarchical Spiking Neural Networks. In Unknown Host Publication (Vol. 6840, pp. 26-31). Berlin Heidelberg: Springer.