Training of a physical neural network

Data processing: artificial intelligence – Neural network – Structure

Reexamination Certificate

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C706S015000, C706S026000, C977S700000, C977S712000, C977S720000, C977S724000, C977S742000

Reexamination Certificate

active

07398259

ABSTRACT:
Physical neural network systems and methods are disclosed. A physical neural network can be configured utilizing molecular technology, wherein said physical neural network comprises a plurality of molecular conductors, which form neural network connections thereof. A training mechanism can be provided for training said physical neural network to accomplish a particular neural network task based on a neural network training rule. The neural network connections are formed between pre-synaptic and post-synaptic components of said physical neural network. The neural network generally includes dynamic and modifiable connections for adaptive signal processing. The neural network training mechanism can be based, for example, on the Anti-Hebbian and Hebbian (AHAH) rule and/or other plasticity rules.

REFERENCES:
patent: 2707223 (1955-04-01), Hollman
patent: 3222654 (1965-12-01), Widrow et al.
patent: 3833894 (1974-09-01), Aviram et al.
patent: 4802951 (1989-02-01), Clark et al.
patent: 4926064 (1990-05-01), Tapang
patent: 4974146 (1990-11-01), Works et al.
patent: 4988891 (1991-01-01), Mashiko
patent: 5315162 (1994-05-01), McHardy et al.
patent: 5422983 (1995-06-01), Castelaz et al.
patent: 5475794 (1995-12-01), Mashiko
patent: 5589692 (1996-12-01), Reed
patent: 5649063 (1997-07-01), Bose
patent: 5670818 (1997-09-01), Forouhi et al.
patent: 5706404 (1998-01-01), Colak
patent: 5717832 (1998-02-01), Steimle et al.
patent: 5761115 (1998-06-01), Kozicki et al.
patent: 5783840 (1998-07-01), Randall et al.
patent: 5812993 (1998-09-01), Ginosar et al.
patent: 5896312 (1999-04-01), Kozicki et al.
patent: 5904545 (1999-05-01), Smith et al.
patent: 5914893 (1999-06-01), Kozicki et al.
patent: 5951881 (1999-09-01), Rogers et al.
patent: 5978782 (1999-11-01), Neely
patent: 6026358 (2000-02-01), Tomabechi
patent: 6084796 (2000-07-01), Kozicki et al.
patent: 6128214 (2000-10-01), Kuekes et al.
patent: 6245630 (2001-06-01), Ishikawa
patent: 6248529 (2001-06-01), Connolly
patent: 6256767 (2001-07-01), Kuekes et al.
patent: 6282530 (2001-08-01), Huang
patent: 6294450 (2001-09-01), Chen et al.
patent: 6314019 (2001-11-01), Kuekes et al.
patent: 6330553 (2001-12-01), Uchikawa et al.
patent: 6335291 (2002-01-01), Freeman
patent: 6339227 (2002-01-01), Ellenbogen
patent: 6359288 (2002-03-01), Ying et al.
patent: 6363369 (2002-03-01), Liaw et al.
patent: 6383923 (2002-05-01), Brown et al.
patent: 6389404 (2002-05-01), Carson et al.
patent: 6407443 (2002-06-01), Chen et al.
patent: 6418423 (2002-07-01), Kambhatla et al.
patent: 6420092 (2002-07-01), Yang et al.
patent: 6422450 (2002-07-01), Zhou et al.
patent: 6423583 (2002-07-01), Avouris et al.
patent: 6424961 (2002-07-01), Ayala
patent: 6426134 (2002-07-01), Lavin et al.
patent: 6445006 (2002-09-01), Brandes et al.
patent: 6536106 (2003-03-01), Jackson et al.
patent: 6620346 (2003-09-01), Schulz et al.
patent: 6798692 (2004-09-01), Kozicki et al.
patent: 6855329 (2005-02-01), Shakesheff et al.
patent: 2001/0004471 (2001-06-01), Zhang
patent: 2001/0023986 (2001-09-01), Mancevski
patent: 2001/0024633 (2001-09-01), Lee et al.
patent: 2001/0031900 (2001-10-01), Margrave et al.
patent: 2001/0041160 (2001-11-01), Margrave et al.
patent: 2001/0044114 (2001-11-01), Connolly
patent: 2002/0001905 (2002-01-01), Choi et al.
patent: 2002/0004028 (2002-01-01), Margrave et al.
patent: 2002/0004136 (2002-01-01), Gao et al.
patent: 2002/0030205 (2002-03-01), Varshavsky
patent: 2002/0075126 (2002-06-01), Reitz et al.
patent: 2002/0090468 (2002-06-01), Goto et al.
patent: 2002/0086124 (2002-07-01), Margrave et al.
patent: 2002/0102353 (2002-08-01), Mauthner et al.
patent: 2003/0031438 (2003-02-01), Kambe et al.
patent: 2003/0177450 (2003-09-01), Nugent
patent: 2003/0236760 (2003-12-01), Nugent
patent: 2004/0039717 (2004-02-01), Nugent
patent: 2004/0150010 (2004-08-01), Snider
patent: 2004/0153426 (2004-08-01), Nugent
patent: 2004/0162796 (2004-08-01), Nugent
patent: 2004/0193558 (2004-09-01), Nugent
patent: 1 022 764 (2000-01-01), None
patent: 1 046 613 (2000-04-01), None
patent: 1 100 106 (2001-05-01), None
patent: 1 069 206 (2001-07-01), None
patent: 1 115 135 (2001-07-01), None
patent: 1 134 304 (2001-09-01), None
patent: 2071126 (1996-06-01), None
patent: WO 00/44094 (2000-07-01), None
patent: WO 03/017282 (2001-08-01), None
Kevin Gurney, “An Introduction to Neural Networks”, 1999, pp. 39,51 and 115.
Ellenbogen et al., “Architecture for Molecular Electronic Computers: 1. Logic Structures and an Adder Design from Molecular Electronic Diodes”, 2000, pp. 386-426.
Bégin et al. “Categorization in Unsupervised Neural Networks: The Eidos Mode” 1996, pp. 147-154.
Ellenbogen et al., “Architecture for Molecular Electronic Computers: 1 Logic Structures and an , ˜dderDesign from Molecular Electronic Diodes”, 2000, pp. 386-426.
Nanoparticles Get Wired, Dimes Institute, Delft University of Technology, 1997.
A. Bezryadin,Trapping Single Particle with Nanoelectrodes, Physics News Graphics, Sep. 1997.
Snow, et al.,Nanofabrication with Proximal Probes, Proceedings of the IEEE, Apr. 1997.
P. O'Connor, G. Gramegna, P. Rehak, F. Corsi, C. Marzocca,CMOS Preamplifier with High Linearity and Ultra Low Noise for X-Ray.
Spectroscopy, IEEE Transactions on Nuclear Science, vol. 44, No. 3, Jun. 1997, pp. 318-325.
Peter Weiss, “Circuitry in a Nanowire: Novel Growth Method May Transform Chips,” Science News Online, vol. 161, No. 6; Feb. 9, 2002.
Press Release, “Nanowire-based electronics and optics comes one step closer,” Eureka Alert, American Chemical Society; Feb. 1, 2002.
Weeks et al., “High-pressure nanolithography using low-energy electrons from a scanning tunneling microscope,” Institute of Physics Publishing, Nanotechnology 13 (2002), pp. 38-42; Dec. 12, 2001.
CMP Cientifica, “Nanotech: the tiny revolution”; CMP Cientifica, Nov. 2001.
Diehl, et al., “Self-Assembled, Deterministic Carbon Nanotube Wiring Networks,” Angew. Chem. Int. Ed. 2002, 41, No. 2; Received Oct. 22, 2001.
G. Pirio, et al., “Fabrication and electrical characteristics of carbon nanotube field emission microcathodes with an integrated gate electrode,” Institute of Physics Publishing, Nanotechnology 13 (2002), pp. 1-4, Oct. 2, 2001.
Leslie Smith, “An introduction to Neural Networks,” Center for Cognitive and Computational Neuroscience, Dept. of Computing & Mathematics, University of Stirling, Oct. 25, 1996; http//www.cs.stir.ac.uk/˜lss/NNIntro/InvSlides.html.
V. Derycke et al., “Carbon Nanotube Inter- and Intramolecular Logic Gates,” American Chemical Society, Nano Letters, XXXX, vol. 0, No. 0, A-D.
Mark K. Anderson, “Mega Steps Toward the Nanochip,” Wired News, Apr. 27, 2001.
Collins et al., “Engineering Carbon Nanotubes and Nanotube Circuits Using Electrical Breakdown,” Science, vol. 292, pp. 706-709, Apr. 27, 2001.
Landman et al., “Metal-Semiconductor Nanocontacts: Silicon Nanowires,” Physical Review Letters, vol. 85, No. 9, Aug. 28, 2000.
John G. Spooner, “Tiny tubes mean big chip advances,” Cnet News.com, Tech News First, Apr. 26, 2001.
Jeong-Mi Moon et al., “High-Yield Purification Process of Singlewalled Carbon Nanotubes,” J. Phys. Chem. B 2001, 105, pp. 5677-5681.
“A New Class of Nanostructure: Semiconducting Nanobelts Offer Potential for Nanosensors and Nanoelectronics,” Mar. 12, 2001, http://www.sciencedaily.com/releases/2001/03/010309080953.htm.
Hermanson et al., “Dielectrophoretic Assembly of Electrically Functional Microwires from Nanoparticle Suspensions,” Materials Science, vol. 294, No. 5544, Issue of Nov. 2, 2001, pp. 1082-1086.
Press Release, “Toshiba Demonstrates Operation of Single-Electron Transistor Circuit at Room Temperature,” Toshiba, Jan. 10, 2001.
J. Appenzeller et al., “Optimized contact configuration for the study of transport phenomena in ropes of single-wall carbon nanotubes,” Applied P

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