Data processing: artificial intelligence – Neural network – Structure
Reexamination Certificate
2005-10-18
2005-10-18
Knight, Anthony (Department: 2121)
Data processing: artificial intelligence
Neural network
Structure
C706S015000
Reexamination Certificate
active
06957204
ABSTRACT:
A neurocomputer (50) comprises n oscillating processing elements (60A,60B,60C,60D and60E) that communicate through a common medium (70) so that there are required only n connective junctions (80A,80B,80C,80D and80E). A rhythmic external forcing input (90) modulates the oscillatory frequency of the medium (70) which, in turn, is imparted to the n oscillators (60A,60B,60C,60D and60E). Any two oscillators oscillating at different frequencies may communicate provided that input's power spectrum includes the frequency equal to the difference between the frequencies of the two oscillators in question. Thus, selective communication, or dynamic connectivity, between different neurocomputer oscillators occurs due to the frequency modulation of the medium (70) by external forcing.
REFERENCES:
patent: 4660166 (1987-04-01), Hopfield
patent: 4815475 (1989-03-01), Burger
patent: 4926064 (1990-05-01), Tapang
patent: 5072130 (1991-12-01), Dobson
patent: 5263122 (1993-11-01), Nunally
patent: 5446828 (1995-08-01), Woodall
patent: 5479577 (1995-12-01), Yang
patent: 5705956 (1998-01-01), Neely
Synchronization of MEMS resonators and mechanical neurocomputing Hoppensteadt, F.C.; Izhikevich, E.M.;Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on vol.: 48, Issue: 2, Feb. 2001 pp.: 133-138.
Recurrent oscillatory networks of associative memory with Hebbian learning algorithm Kuzmina, M.; Manykin, E.; Surina, I.;Neuroinformatics and Neurocomputers, 1995., Second International Symposium on , Sep. 20-23, 1995 pp.: 246-253..
Influence of stimuli on the comportment of an artificial neural population Druaux, F.; Gorka, M.; Faure, A.; Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on , Oct. 7-10, 1992 pp.: 846-854 vol. 2.
Comparative estimation of the role of specific and unspecific inputs at the arrangement of barrels interaction in the rat somatic cortexSukhov, A.G.; Neuroinformatics and Neurocomputers, 1995., Second International Symposium on , 1995 pp.: 207-209.
Study of the role of rhythmic activity in fixation and reproduction of information on the model of neuronlike elements network Shulgina, G.I.; Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on , 1992 pp.: 1098-1106 vol. 2.
Pattern recognition via synchronization in phase-locked loop neural networks Hoppensteadt, F.C.; Izhikevich, E.M.; Neural Networks, IEEE Transactions on ,vol.: 11 , Issue: 3 , May 2000 pp.: 734-738.
Diffusion bay simulation and its impact on the overall FAB performance: a simplified exampleCollins, D.W.; Flores-Godoy, J.-J.; Tsakalis, K.S.; Hoppensteadt, F.C.; Semiconductor Manufacturing, 2003 IEEE International Symposium on , pp.: 315-318.
Thalamo-cortical interactions modeled by forced weakly connected oscillatory networks Hoppensteadt, F.C.; Izhikevich, E.M.; Neural Networks, 1997., International Conference on ,vol.: 1 , Jun. 9-12, 1997, pp.: 328-331 vol. 1.
Canonical models for mathematical neuroscience Hoppensteadt, F.C.; Izhikevich, E.M.; Neural Networks,1997., International Conference on ,vol.: 1 , Jun. 9-12, 1997 pp.: 324-327 vol. 1.
Investigation of minimum inventory variability scheduling policies in a large semiconductor manufacturing facility Collins, D.W.; Hoppensteadt, F.C.;American Control Conference, 1997. Proceedings of the 199, vol.: 3 , 1997, pp.: 1924-1928.
Associative memory of weakly connected oscillators Hoppensteadt, F.C.; Izhikevich, E.M.; Neural Networks,1997., International Conference on, vol.: 2, Jun. 9-12, 1997, pp.: 1135-1138 vol. 2.
Computer simulation of a neural prism Hoppensteadt, F.C.; Circuits and Systems, 1989., Proceedings of the 32nd Midwest Symposium on , Aug. 14-16, 1989, pp.: 238-239 vol. 1.
Fast numerical integration of relaxation oscillator networks based on singular limit solutions Linsay, P.S.; DeLiang L. Wang; Neural Networks, IEEE Transactions on , vol.: 9 Issue: 3 , May 1998 Page(s): 523-532.
The dynamics of coupled nonlinear oscillators: from relaxation oscillators to neurons Linsay, P.S.; Brailove, A.A.; Gaudiano, P.; Przybyszewski, A.; Wilson, C.; Circuits and Systems, 1996 IEEE Intl. Symposium on , vol.: 3 Page(s) 138-141.
Adaptive Synchronization of Neural and Physical Oscilators, Kenji Doya, Shuji Yoshizawa, Advances in Neural Information Processing Systems 4, 109-116, (1992).
Designing Rhythmic Motions using Neural Oscillators, MJatthew . Williamson, Proceeding of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pps. 494-500.
Rhythmic Robot Arm Control Using Oscillators, Matthew M. Williamson, Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE pps. 77-83.
Hopfield, J. J., “Neural networks and physical systems with emergent collective computational abilities” Proc. Natl. Acad. Sci. USA, vol. 79, pp. 2554-2558, Apr. 1982.
Hoppensteadt, F. C., Izhikevich, E. M., “Oscillatory Neurocomputers with Dynamic Connectivity” Physical Review Letters, vol. 81, No. 14, Apr. 5, 1999.
Linares-Barranco, B. et al., “CMOS Analog Neural Network Systems Based On Oscillatory Neurons” 1992 IEEE Intl. Symposium, vol. 1 of 6, pp. 2236-2238.
Kurokawa, H. et al., “A Local Connected Neural Oscillator Network for Sequential Character Segmentation” 1997 IEEE Intl. Conf. on Neural Networks, pp. 838-843, Jun. 9-12.
Buhmann, J. et al., “Sensory Segmentation by Neural Oscillators” IJCNN, vol. 1, pp. II603-607, Jul. 8-12, 1991.
Endo, S. et al., “Neural Network with Interacting Oscillators to Generate Low Frequency Rhythm” IEEE Intl. Conf., vol. 12, pp. 1445-1446, Nov. 1-4, 1990.
Lange, T. E. et al., “Phase-Locking of Artificial Neural Oscillators Can Perform Dynamic Role-Binding and Inferencing” IJCNN, vol. 1, p. II595, Jun. 18-22, 1989.
Wang, DeLiang “An Oscillation Model of Auditory Stream Segregation” IAPR, vol. III, pp. 198-200, Oct. 9-13, 1994.
Liu, Wei-Ping et al. “Phase-Locked Loop with Neurocontroller” SICE, pp. 1133-1138. Jul. 20-31, 1998.
Grossberg, S. “Nonlinear Neural Networks: Principles Mechanisms, and Architectures”, Neural Networks, vol. 1, No. 1, pp. 17-61, 1988.
Hoppensteadt Frank C.
Izhikevich Eugene
Arizona Board of Regents
Gallagher & Kennedy P.A.
Holmes Michael B.
Knight Anthony
MacBlain Thomas D.
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