Abstract
In this paper the four-dimensional dynamic diffusing mechanism and the enhancement in Long-Term Potentiation (LTP) of intrinsic nitric oxide (NO) in nervous system are studied computationally. A novel unsupervised Diffusing Self-Organizing Maps (DSOM) model is presented on the union of SOM with NO diffusing mechanism. Based on the spatial prototype mapping, temporal enhancement is introduced in DSOM and the fine-tuning manner is improved by the simplified NO diffusing mechanism. Furthermore, the quantization error of optimal weights is valuated and the detailed noise analysis of DSOM is presented. Finally some typical stimulation experiments are presented to illustrate how DSOM gracefully handles time warping and multiple patterns with overlapping reference vectors.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Kohonen, T., Self-Organizing Maps, 2nd ed., Berlin: Springer-Verlag, 1997, 145–152.
Bradt, D. S., Snyder, S. H., Nitric oxide, A novel neuronal messenger, Neuron, 1992, 8: 3–11.
Krekelberg, B., Taylor, J. G., Nitric Oxide: A diffusion messenger in a topographic map, in Proc. Int. Conf. on Artifical Neural Networks, Paris: Springer, 1995, 1023–1028.
Krekelberg, B., Taylor, J. G., Nitric oxide and the development of long-range horizontal connectivity, Neural Networks World, 1996, 6: 185–189.
Garthwaite, J., Charles, S., Chess-Williams, R., Endothelium-derived relaxing factor release on activation of NMDA receptors suggests role as interneurons messager in the brain, Nature, 1998, 336: 385–388.
Furchgott, R. F., Ignarro, L. J., Ferid Murad, Discoveries concerning nitric oxide as a signalling molecule in the cardiovascular system, Nobel Prize, 1998, http://www.apnet.com/inscight/10121998/grapha.htm.
Kohonen, T., Physiological interpretation of the self-organizing map algorithm, Neural Networks, 1993, 6: 895–905.
Kohonen, T., The self-organizing map, Neurocomputing, 1998, 21: 1–6.
Philippides, A., Husbands, P., O’shea, M., Neuronal signalling: It’s a gas! in Proceedings of the 8th International Conference on Artificial Neural Networks, Berlin: Springer-Verlag, 1998, 979–984.
Vaughn, M., Kuo, L., Liao, J., Effective diffusion distance of nitric oxide in the microcirculation, Am. J. Physiol., 1998, 274: 1705–1714.
Vaughn, M., Kuo, L., Liao, J., Estimation of nitric oxide production and reaction rates in tissue by use of a mathematical model, Am. J. Physiol., 1998, 274: 2163–2176.
Krekelberg, B., Taylor, J. G., Nitric oxide in cortical map formation, Journal of Chemical Neuroanatomy, 1996, 10(3): 191–196.
Philippides, A., Husbands, P., O’shea, M., Four-dimensional neuronal signalling by nitric oxide: a computational analysis, J. NeuroSci., 2000, 20(3): 1199–1207.
Smith, T. M. C., Philippides, A., Nitric oxide signalling in real and artificial neural networks, British Telecom Technology Journal, 2000, 18(4): 40–149.
Carslaw, H., Jaeger, J., Conduction of Heat in Solids, New York: Oxford UP, 1959.
Crank, J., The Mathematics of Diffusion, New York: Oxford UP, 1980.
Malinski, T., Taha, Z., Grunfeld, S. et al., Diffusion of nitric oxide in the aorta wall monitored in situ by porphyrinic microsensors, Biochem. Biophys. Res. Commun., 1993, 193: 1076–1082.
Wood, J., Garthwaite, J., Models of the diffusional spread of nitric oxide: implications for neural nitric oxide signalling and its pharmacological properties, Neuropharmacology, 1994, 33: 1235–1244.
Grossberg, S., Classical and instrumental learning by neural networks, Progress in Theoretical Biology, New York: Academic PRess, 1974, 3: 51–141.
de Bodt, E., Cottrel, M., Verleysen, M., Statistical tools assess the reliability of self-organizing maps, Neural Networks, 2002, 15(special issue): 967–978.
Hirose, A., Nagashima, T., Predictive self-organizing map for vector quantization of migratory signals and its application to mobile communications, Neural Networks, 2003, 14(6): 1532–1540.
Fritzke, B., Growing cell structure — a self-organizing network for unsupervised and supervised learning, Neural Networks, 1994, 7: 1441–1460.
Zhou, Z. T., Hu, D. W., Growing group model for self-organized learning, IEEE Int. Conf. on Neural Information Processing, Beijing: Springer, 1995, 1: 325–328.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yin, J., Hu, D., Chen, S. et al. DSOM: a novel self-organizing model based on NO dynamic diffusing mechanism. Sci China Ser F 48, 247–262 (2005). https://doi.org/10.1360/04yf0116
Received:
Issue Date:
DOI: https://doi.org/10.1360/04yf0116