References
- Broadbent, D. (1982). Task combination and selective intake of information, Acta Psychologica 50(3): 253-290.
- Desimone, R. and Duncan, J. (1995). Neural mechanisms of selective visual-attention, Annual Review of Neuroscience 18(1): 193-222.
- Duch, W. and Jankowski, N. (1999). Survey of neural transfer functions, Neural Computing Surveys 2(1): 163-212.
- Durbin, R. and Rumelhart, D. (1990). Product units: A computationally powerful and biologically plausible extension to backpropagation networks, Neural Computation 1(1): 133-142.
- Feldman, J. and Ballard, D. (1982). Connectionist models and their properties, Cognitive Science 6(3): 205-254.
- 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, International Journal of Applied Mathematics and Computer Science 19(1): 113-126, DOI: 10.2478/v10006-009-0011-9.
- 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, Physical Review B 57(7): 4017-4026.
- Gupta, M. (2008). Correlative type higher-order neural units with applications, IEEE International Conference on Automation and Logistics, ICAL 2008, Qingdao, China, pp. 715-718.
- Hager, G. and Toyama, K. (1999). Incremental focus of attention for robust visual tracking, International Journal of Computer Vision 35(1): 45-63.
- Houghton, G. and Tipper, S. (1996). Inhibitory mechanisms of neural and cognitive control: Applications to selective attention and sequential action, Brain and Cognition 30(1): 20-43.
- Huk, M. (2004). The sigma-if neural network as a method of dynamic selection of decision subspaces for medical reasoning systems, Journal of Medical Informatics & Technologies 7(1): 65-73.
- Huk, M. (2006). Sigma-if neural network as a use of selective attention technique in classification and knowledge discovery problems solving, Annales UMCS Informatica AI 5(2): 121-131.
- Huk, M. (2009). Learning distributed selective attention strategies with the Sigma-if neural network, in M. Akbar and D. Hussain (Eds.), Advances in Computer Science and IT, In-Tech, Vukovar, pp. 209-232.
- Indiveri, G. (2008). Neuromorphic VLSI models of selective attention: From single chip vision sensors to multi-chip systems, Sensors 8(9): 5352-5375.
- Korbicz, J., Obuchowicz, A. and Uciński, D. (1994). Unidirectional networks, in L. Bolc (Ed.), Artificial Neural Networks: Foundations and Applications, Akademicka Oficyna Wydawnicza PLJ, Warsaw, pp. 35-58.
- Körding, K. and König, P. (2001). Neurons with two sites of synaptic integration learn invariant representations, Neural Computation 13(12): 2823-2849.
- Mel, B. (1990). The sigma-pi column: A model of associative learning in cerebral cortex, Technical report, CNS Memo 6, Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA.
- Mel, B. (1992). The clusteron: Toward a simple abstraction for a complex neuron, in J. Moody, S. Hanson and R. Lippmann (Eds.), Advances in Neural Information Processing Systems, Vol. 4, Morgan Kaufmann, San Mateo, CA, pp. 35-42.
- Neville, R. and Eldridge, S. (2002). Transformations of sigmapi nets: Obtaining reflected functions by reflecting weight matrices, Neural Networks 15(3): 375-393.
- Niebur, E., Hsiao, S. and Johnson, K. (2002). Synchrony: A neuronal mechanism for attentional selection?, Current Opinion in Neurobiology 12(2): 190-194.
- Noh, T., Song, P. and Sievers, A. (1991). Self-consistency conditions for the effective-medium approximation in composite materials, Physical Review B 44(11): 5459-5464.
- Noton, D. and Stark, L. (1971). Scanpaths in saccadic eye movements while viewing and recognizing patterns, Vision Research 11(9): 929-942.
- 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, The Journal of Neuroscience 13(11): 4700-4719.
- Pedro, J. O. and Dahunsi, O. A. (2011). Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system, International Journal of Applied Mathematics and Computer Science 21(1): 137-147, DOI: 10.2478/v10006-011-0010-5.
- Raczkowski, D., Canning, A. and Wang, L. (2001). Thomas-fermi charge mixing for obtaining self-consistency in density functional calculations, Physical Review B 64(12): 121101-121105.
- Rumelhart, D., Hinton, G. and McClelland, J. (1986). A general framework for parallel distributed processing, in D. Rumelhart and J. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, Vol. 1, The MIT Press, Cambridge, MA, pp. 45-76.
- Stark, L., Privitera, C. and Azzariti, M. (2000). Locating regions-of-interest for the mars rover expedition, International Journal of Remote Sensing 21(17): 3327-3347.
- Treisman, A. (1960). Contextual cues in selective listening, Quarterly Journal of Experimental Psychology 12(4): 242-248.
- Tsotsos, J., Culhane, S. and Cutzu, F. (2001). From foundational principles to a hierarchical selection circuit for attention, in J. Braun, C. Koch and J. Davis (Eds.), Visual Attention and Cortical Circuits, MIT Press, Cambridge, MA, pp. 285-306.
- Vanrullen, R. and Koch, C. (2003). Visual selective behavior can be triggered by a feed-forward process, Journal of Cognitive Neuroscience 15(2): 209-217.
- Weber, C. and Wermter, S. (2007). A self-organizing map of sigma-pi units, Neurocomputing 70(13-15): 2552-2560.