Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
IEEE Transactions on Computers
Adaptive co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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The human visual system demonstrates powerful image processing functionalities. Inspired by the principles from neuroscience, a spiking neural network is proposed to perform the discrete cosine transform for visual images. The structure and the properties of the network are detailed in this paper. Simulation results show that the network is able to perform the discrete cosine transform for visual images. Based on this mechanism, the key features can be extracted in ON/OFF neuron arrays. These key features can be used to reconstruct the visual images. The network can be used to explain how the spiking neuron-based system can perform key feature extraction. The differences between the discrete cosine transform and the spiking neural network transform are discussed.