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Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network Cover

Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

International Journal of Applied Mathematics and Computer Science
Volume 22 (2012): Issue 2 (June 2012)
By:   
Open Access
|Jun 2012
References

References

  1. Broadbent, D. (1982). Task combination and selective intake of information,(3): 253-290.
    Open DOISearch in Google Scholar
  2. Desimone, R. and Duncan, J. (1995). Neural mechanisms of selective visual-attention,(1): 193-222.
    Open DOISearch in Google Scholar
  3. Duch, W. and Jankowski, N. (1999). Survey of neural transfer functions,(1): 163-212.
    Search in Google Scholar
  4. Durbin, R. and Rumelhart, D. (1990). Product units: A computationally powerful and biologically plausible extension to backpropagation networks,(1): 133-142.
    Open DOISearch in Google Scholar
  5. Feldman, J. and Ballard, D. (1982). Connectionist models and their properties,(3): 205-254.
    Open DOISearch in Google Scholar
  6. Ferguene, F. and Toumi, F. F. (2009). Dynamic external force feedback loop control of a robot manipulator using a neural compensator—Application to the trajectory following in an unknown environment,(1): 113-126, DOI: 10.2478/v10006-009-0011-9.
    Open DOISearch in Google Scholar
  7. Fonseca, L., Jimenez, J., Leburton, J. and Martin, R. (1998). Self-consistent calculation of the electronic structure and electron-electron interaction in self-assembled InAs-GaAs quantum dot structures,(7): 4017-4026.
    Open DOISearch in Google Scholar
  8. Gupta, M. (2008). Correlative type higher-order neural units with applications,, pp. 715-718.
    Search in Google Scholar
  9. Hager, G. and Toyama, K. (1999). Incremental focus of attention for robust visual tracking,(1): 45-63.
    Open DOISearch in Google Scholar
  10. Houghton, G. and Tipper, S. (1996). Inhibitory mechanisms of neural and cognitive control: Applications to selective attention and sequential action,(1): 20-43.
    Open DOISearch in Google Scholar
  11. Huk, M. (2004). The sigma-if neural network as a method of dynamic selection of decision subspaces for medical reasoning systems,(1): 65-73.
    Search in Google Scholar
  12. Huk, M. (2006). Sigma-if neural network as a use of selective attention technique in classification and knowledge discovery problems solving,(2): 121-131.
    Search in Google Scholar
  13. Huk, M. (2009). Learning distributed selective attention strategies with the Sigma-if neural network,M. Akbar and D. Hussain (Eds.),, In-Tech, Vukovar, pp. 209-232.
    Open DOISearch in Google Scholar
  14. Indiveri, G. (2008). Neuromorphic VLSI models of selective attention: From single chip vision sensors to multi-chip systems,(9): 5352-5375.
    Open DOISearch in Google Scholar
  15. Korbicz, J., Obuchowicz, A. and Uciński, D. (1994). Unidirectional networks,L. Bolc (Ed.),, Akademicka Oficyna Wydawnicza PLJ, Warsaw, pp. 35-58.
    Search in Google Scholar
  16. Körding, K. and König, P. (2001). Neurons with two sites of synaptic integration learn invariant representations,(12): 2823-2849.
    Open DOISearch in Google Scholar
  17. Mel, B. (1990). The sigma-pi column: A model of associative learning in cerebral cortex,, CNS Memo 6, Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA.
    Search in Google Scholar
  18. Mel, B. (1992). The clusteron: Toward a simple abstraction for a complex neuron,J. Moody, S. Hanson and R. Lippmann (Eds.),, Vol. 4, Morgan Kaufmann, San Mateo, CA, pp. 35-42.
    Search in Google Scholar
  19. Neville, R. and Eldridge, S. (2002). Transformations of sigmapi nets: Obtaining reflected functions by reflecting weight matrices,(3): 375-393.
    Open DOISearch in Google Scholar
  20. Niebur, E., Hsiao, S. and Johnson, K. (2002). Synchrony: A neuronal mechanism for attentional selection?,(2): 190-194.
    Open DOISearch in Google Scholar
  21. Noh, T., Song, P. and Sievers, A. (1991). Self-consistency conditions for the effective-medium approximation in composite materials,(11): 5459-5464.
    Open DOISearch in Google Scholar
  22. Noton, D. and Stark, L. (1971). Scanpaths in saccadic eye movements while viewing and recognizing patterns,(9): 929-942.
    Open DOISearch in Google Scholar
  23. Olshausen, B., Anderson, C. and Van Essen, D. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information,(11): 4700-4719.
    Open DOISearch in Google Scholar
  24. Pedro, J. O. and Dahunsi, O. A. (2011). Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system,(1): 137-147, DOI: 10.2478/v10006-011-0010-5.
    Open DOISearch in Google Scholar
  25. Raczkowski, D., Canning, A. and Wang, L. (2001). Thomas-fermi charge mixing for obtaining self-consistency in density functional calculations,(12): 121101-121105.
    Open DOISearch in Google Scholar
  26. Rumelhart, D., Hinton, G. and McClelland, J. (1986). A general framework for parallel distributed processing,D. Rumelhart and J. McClelland (Eds.),, Vol. 1, The MIT Press, Cambridge, MA, pp. 45-76.
    Search in Google Scholar
  27. Stark, L., Privitera, C. and Azzariti, M. (2000). Locating regions-of-interest for the mars rover expedition,(17): 3327-3347.
    Open DOISearch in Google Scholar
  28. Treisman, A. (1960). Contextual cues in selective listening,(4): 242-248.
    Open DOISearch in Google Scholar
  29. Tsotsos, J., Culhane, S. and Cutzu, F. (2001). From foundational principles to a hierarchical selection circuit for attention,J. Braun, C. Koch and J. Davis (Eds.),, MIT Press, Cambridge, MA, pp. 285-306.
    Search in Google Scholar
  30. Vanrullen, R. and Koch, C. (2003). Visual selective behavior can be triggered by a feed-forward process,(2): 209-217.
    Open DOISearch in Google Scholar
  31. Weber, C. and Wermter, S. (2007). A self-organizing map of sigma-pi units,(13-15): 2552-2560.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10006-012-0034-5 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 449 - 459
Published on: Jun 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
artificial neural networks,
selective attention,
self consistency,
error backpropagation,
delta rule
Related subjects:
Mathematics,
Applied mathematics

© 2012 Maciej Huk, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 22 (2012): Issue 2 (June 2012)
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