PCNN models and applications

The pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNN's are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in new detail.

[1]  Christof Koch,et al.  Modeling the mammalian visual system , 1989 .

[2]  John L. Johnson,et al.  Pulse coupled neural networks (PCNN), wavelets and radial basis functions: olfactory sensor applications , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[3]  Clark S. Lindsey,et al.  Wavelets and signal processing , 1996, Defense + Commercial Sensing.

[4]  J. J. Hopfield,et al.  Pattern recognition computation using action potential timing for stimulus representation , 1995, Nature.

[5]  Nabil H. Farhat,et al.  Complexity and chaotic dynamics in a spiking neuron embodiment , 1994, Defense + Commercial Sensing.

[6]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[7]  M. Arndt,et al.  A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations , 1989 .

[8]  John G. Taylor,et al.  Storing temporal sequences , 1991, Neural Networks.

[9]  Manuel Samuelides,et al.  Intelligent sensors research using pulse-coupled neural networks for focal plane image processing , 1996, Defense + Commercial Sensing.

[10]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[11]  C. Koch,et al.  Multiple channels and calcium dynamics , 1989 .

[12]  W. Rall.  Cable theory for dendritic neurons , 1989 .

[13]  J. L. Johnson.  Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images. , 1994, Applied optics.

[14]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[15]  M. L. Padgett,et al.  Pulse coupled neural networks (PCNN) and wavelets: biosensor applications , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[16]  A S French,et al.  A flexible neural analog using integrated circuits. , 1970, IEEE transactions on bio-medical engineering.

[17]  Reinhard Eckhorn,et al.  A neural network for scene segmentation by temporal coding , 1996, Neurocomputing.

[18]  John L. Johnson.  Pulse-coupled neural networks , 1994, Defense + Commercial Sensing.

[19]  Heggere S. Ranganath,et al.  A neural network for image understanding , 1997 .

[20]  Jason M. Kinser.  Object isolation using a pulse-coupled neural network , 1996, Optics & Photonics.

[21]  DeLiang Wang,et al.  Emergent synchrony in locally coupled neural oscillators , 1995, IEEE Trans. Neural Networks.

[22]  J. L. Johnson,et al.  Observation of periodic waves in a pulse-coupled neural network. , 1993, Optics letters.

[23]  Bartlett W. Mel,et al.  Complex-Cell Responses Derived from Center-Surround Inputs: The Surprising Power of Intradendritic Computation , 1996, NIPS.

[24]  John L. Johnson,et al.  Pulse-coupled neural networks (PCNN) and new approaches to biosensor applications , 1998, Defense, Security, and Sensing.

[25]  Mary Lou Padgett,et al.  Digital X-ray image processing using biologically inspired methods , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[26]  Reinhard Eckhorn,et al.  Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[27]  Lance M. Optican,et al.  Temporal Codes for Colors, Patterns, and Memories , 1994 .

[28]  M. L. Padgett,et al.  Multiscale image factorization , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[29]  John L. Johnson,et al.  Globally stable saturable learning laws , 1991, Neural Networks.

[30]  Paul C. Bressloff.  Dynamics of a compartmental model integrate-and-fire neuron with somatic potential reset , 1995 .

[31]  Jeffery Johnson,et al.  Pulse coupled neural networks for image processing , 1995, Proceedings IEEE Southeastcon '95. Visualize the Future.

[32]  John G. Taylor,et al.  Dynamics of compartmental model neurons , 1994, Neural Networks.

[33]  C. Koch,et al.  Towards a neurobiological theory of consciousness , 1990 .

[34]  Thomas Lindblad,et al.  Parameter adaptation in a simplified pulse-coupled neural network , 1999, Other Conferences.

[35]  Manuel Samuelides,et al.  Temporal encoding of multispectral satellite imagery for segmentation using pulsed coupled neural networks , 1995, Remote Sensing.

[36]  Colin Giles,et al.  Learning, invariance, and generalization in high-order neural networks. , 1987, Applied optics.

[37]  H. John Caulfield,et al.  Pulse-coupled neural network sensor fusion , 1998, Defense, Security, and Sensing.

[38]  Idan Segev,et al.  Compartmental models of complex neurons , 1989 .