Perfect recall from noisy input patterns with a dendritic lattice associative memory

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 503-510

  Ritter, Gerhard X ^a   Urcid, Gonzalo ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRAIC OPERATIONS; ASSOCIATIVE MEMORIES; CONVERGENCE PROBLEMS; DENDRITIC COMPUTING; INPUT PATTERNS; TRAINING PHASE;

ASSOCIATIVE PROCESSING; ASSOCIATIVE STORAGE;

NEURAL NETWORKS;

EID: 80054744200     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033263     Document Type: Conference Paper

References (31)

1. J. C. Eccles, The Understanding of the Brain. McGraw-Hill, New York, NY., 1977.
2. C. Koch and I. Segev, Methods in Neuronal Modeling: From Synapses to Networks. MIT Press, Boston, MA., 1989.
3. M. A. Arbib, The Handbook of Brain Theory and Neural Networks. Chapter Dendritic Processing, I. Segev, pp. 282-289, MIT Press, Boston, MA., 1998.
4. M. A. Arbib, The Handbook of Brain Theory and Neural Networks. MIT Press, Boston, MA., 1998.
5. T. McKenna, J. Davis, and S. F. Zornetzer, Single Neuron Computation. Chapter Electronic Models of Neuron Dendrites and Single Neuron Computation, W.R. Holmes and W. Rall, pp. 7-25, Academic Press, San Diego, CA., 1992.
6. T. McKenna, J. Davis, and S. F. Zornetzer, Single Neuron Computation. Academic Press, San Diego, CA., 1992.
7. B. W. Mel, "Synaptic integration in excitable dendritic trees," Journal of Neurophysiology, vol. 70, pp. 1086-1101, 1993.
8. G. M. Edelman, E.E. Gall, and W. M. Cowan, Synaptic Function. Chapter Functional Possibilities for Synapses on Dendrites and Dendritic Spines, W. Rall and I. Segev, pp. 605-636, John Wiley & Sons, New York, NY., 1987.
9. T. McKenna, J. Davis, and S. F. Zornetzer, Single Neuron Computation. Chapter Canonical Neurons and their Computational Organization, G.M. Shepherd, pp. 27-55, Academic Press, San Diego, CA., 1992.
10. J. L. Davidson and G. X. Ritter, "A theory of morphological neural networks," Proceedings of SPIE, vol. 1215, pp. 378-388, Bellingham, WA., 1990.
11. J. L. Davidson and F. Hummer, "Morphological neural networks: an introduction with applications," IEEE Transactions on Signal Processing, vol. 12, pp. 177-210, 1993.
12. G. X. Ritter and P. Sussner, "An introduction to morphological neural networks," Proceedings 13th Int. Conf. Pattern Recognition, Vienna, Austria, pp. 709-717, 1996.
13. Y. Won, P.D. Gader, and P. C. Coffield, "Morphological shared-weight networks with applications to automatic target recognition," IEEE Transactions on Neural Networks, vol. 8, no. 5, pp. 1195-1203, 1997.
14. G. X. Ritter, P. Sussner, and J. L. Diaz de Leon, "Morphological associative memories," IEEE Transactions on Neural Networks, vol. 9, pp. 281-293, 1998.
15. G. X. Ritter, J. L. Diaz de Leon, and P. Sussner, "Morphological bidirectional associative memories," Neural Networks, vol. 12, pp. 851-867, 1999.
16. L. F. C. Pessoa and P. Maragos, "Neural networks with hybrid morphological-rank-linear nodes: a unifying framework with applications to handwritten character recognition," Pattern Recognition, vol. 33, no. 6, pp. 945-960, 2000.
17. P. D. Gader, M. Khabou, and A. Koldobsky, "Morphological regularization neural networks," Pattern Recognition, vol. 33, no. 6, pp. 935-945, 2000.
18. G.X. Ritter and P. Gader, "Fixed points of lattice transforms and lattice associative memories," in P. Hawkes (Ed.), Advances in Imaging and Electron Physics, vol. 144, 165-242, Elsevier, San Diego, CA, 2006.
19. V. G. Kaburlasos, Towards a Unified Modeling and Knowledge Representation Based on Lattice Theory. Springer, Computational Intelligence, vol. 27, Heidelberg, Germany, 2006.
20. V. G. Kaburlasos and G. X. Ritter, Computational Intelligence Based on Lattice Theory. Springer, Computational Intelligence, vol. 67, Heidelberg, Germany, 2007.
21. V. G. Kaburlasos and G. X. Ritter, Computational Intelligence based on Lattice Theory. Chapter Learning in Lattice Neural Networks that Employ Dendritic Computing, G.X. Ritter and G. Urcid, Springer, Computational Intelligence, vol. 67, pp. 25-44, Berlin, 2007.
22. G. X. Ritter and G. Urcid, "Lattice algebra approach to single-neuron computation," IEEE Transactions on Neural Networks, vol. 14, no. 2, pp. 282-295, 2003.
23. G. X. Ritter and L. Iancu, "Lattice algebra approach to neural networks and pattern classification," Pattern Recognition and Image Analysis, vol. 14, no. 2, pp. 191-198, Nauka/Interperiodica Publishing, Moscow, 2004.
24. G. X. Ritter and L. Iancu, "A morphological auto-associative memory based on dendritic computing," Proceedings of IEEE International Joint Conference in Neural Networks, vol. 2, pp. 915-920, 2004.
25. G. X. Ritter, L. Iancu, and M. S. Schmalz, "A new auto-associative memory based on lattice algebra," Proceedings 9th Iberoamerican Congress on Pattern Recognition, Springer LNCS, vol. 3287, pp. 148-155, 2004.
26. E. B. Corrochano, Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics. Chapter A Lattice Algebra Approach to Neural Computation, G.X. Ritter and L. Iancu, pp. 97-129, Springer, Berlin, Germany, 2005.
27. G. X. Ritter, G. Urcid, and M. S. Schmalz, "Lattice associative memories that are robust in the presence of noise," Proceedings of SPIE: Mathematical Methods in Pattern and Image Analysis, J.T. Astola, I. Tabus, and J. Barrera (Eds), vol. 5916, 59160Q:1-6, Bellingham, WA., 2005.
28. G. X. Ritter and G. Urcid, "Recent developments in lattice neural networks," Research on Computing Science: Advances in Artificial Intelligence and Computer Science, A. Gelbukh, C. Yáñez-M., and O. Camacho-N. (Eds), vol. 14, pp. 1-12, Mexico, D.F., 2005.
29. L. Iancu, Lattice Algebra Approach to Neural Computation. Ph.D. Thesis, University of Florida, Gainesville, FL., 2005.
30. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition. John Wiley & Sons, New York, NY, 2001.
31. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd edition Pearson Prentice-Hall, Upper Sadle River, NJ, 2008.