Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing.[1]

In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex.[2] The Eckhorn model provided a simple and effective tool for studying small mammal’s visual cortex, and was soon recognized as having significant application potential in image processing.

In 1994, Johnson adapted the Eckhorn model to an image processing algorithm, calling this algorithm a pulse-coupled neural network.

The basic property of the Eckhorn's linking-field model (LFM) is the coupling term. LFM is a modulation of the primary input by a biased offset factor driven by the linking input. These drive a threshold variable that decays from an initial high value. When the threshold drops below zero it is reset to a high value and the process starts over. This is different than the standard integrate-and-fire neural model, which accumulates the input until it passes an upper limit and effectively "shorts out" to cause the pulse.

LFM uses this difference to sustain pulse bursts, something the standard model does not do on a single neuron level. It is valuable to understand, however, that a detailed analysis of the standard model must include a shunting term, due to the floating voltages level in the dendritic compartment(s), and in turn this causes an elegant multiple modulation effect that enables a true higher-order network (HON).[3][4][5]

A PCNN is a two-dimensional neural network. Each neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel's color information (e.g. intensity) as an external stimulus. Each neuron also connects with its neighboring neurons, receiving local stimuli from them. The external and local stimuli are combined in an internal activation system, which accumulates the stimuli until it exceeds a dynamic threshold, resulting in a pulse output. Through iterative computation, PCNN neurons produce temporal series of pulse outputs. The temporal series of pulse outputs contain information of input images and can be used for various image processing applications, such as image segmentation and feature generation. Compared with conventional image processing means, PCNNs have several significant merits, including robustness against noise, independence of geometric variations in input patterns, capability of bridging minor intensity variations in input patterns, etc.

A simplified PCNN called a spiking cortical model was developed in 2009.[6]

Applications

PCNNs are useful for image processing, as discussed in a book by Thomas Lindblad and Jason M. Kinser.[7]

PCNNs have been used in a variety of image processing applications, including: image segmentation, pattern recognition, feature generation, face extraction, motion detection, region growing, image denoising[8] and image enhancement [9]

Multidimensional pulse image processing of chemical structure data using PCNN has been discussed by Kinser, et al.[10]

They have also been applied to an all pairs shortest path problem.[11]

References

  1. ^ Zhan, K.; Shi, J.; Wang, H.; Xie, Y.; Li, Q. (2017). "Computational mechanisms of pulse-coupled neural networks: A comprehensive review". Archives of Computational Methods in Engineering. 24 (3): 573–588. doi:10.1007/s11831-016-9182-3. S2CID 57453279.
  2. ^ Eckhorn, R.; Reitbock H.J.; Arndt, M.; Dicke, P. (1989). "A neural network for feature linking via synchronous activity: results from cat visual cortex and from simulations". In Cotterill, R.M.J. (ed.). Models of Brain Function. Cambridge: Cambridge University Press. pp. 255–272.
  3. ^ Johnson and Padgett (1999). "PCNN models and applications". IEEE TRANSACTIONS ON NEURAL NETWORKS. 10 (3, MAY): 480–498. doi:10.1109/72.761706. for the shunting terms
  4. ^ Giles, C. Lee; Sun, Guo-Zheng; Chen, Hsing-Hen; Lee, Yee-Chun; Chen, Dong (1989). "Higher Order Recurrent Networks and Grammatical Inference". Advances in Neural Information Processing Systems. 2. Retrieved 2024-03-12.
  5. ^ Giles, C.; Griffin, R.; Maxwell, T. (1987). "Encoding Geometric Invariances in Higher-Order Neural Networks". Neural Information Processing Systems. 0. Retrieved 2024-03-12.
  6. ^ Kun Zhan; Hongjuan Zhang; Yide Ma (December 2009). "New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing". IEEE Transactions on Neural Networks. 20 (12): 1980–1986. doi:10.1109/TNN.2009.2030585.((cite journal)): CS1 maint: multiple names: authors list (link)
  7. ^ Lindblad, Thomas; Kinser, Jason M. (2005). Image processing using pulse-coupled neural networks (2nd, rev. ed.). Berlin ; New York: Springer. ISBN 3-540-24218-X.
  8. ^ Zhang, Y. (2008). "Improved Image Filter based on SPCNN". Science in China Series F. 51 (12): 2115–2125. doi:10.1007/s11432-008-0124-z. S2CID 22319368.
  9. ^ Wu, L. (2010). "Color Image Enhancement based on HVS and PCNN". Science China Information Sciences. 53 (10): 1963–1976. doi:10.1007/s11432-010-4075-9.
  10. ^ Kinser, Jason M; Waldemark, Karina; Lindblad, Thomas; Jacobsson, Sven P (May 2000). "Multidimensional pulse image processing of chemical structure data". Chemometrics and Intelligent Laboratory Systems. 51 (1): 115–124. doi:10.1016/S0169-7439(00)00065-4.
  11. ^ Wei, G.; Wang, S. (2011). "A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network". Digital Signal Processing. 21 (4): 517–521. doi:10.1016/j.dsp.2011.02.004.
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