EarthWeb   
HomeAccount InfoLoginSearchMy ITKnowledgeFAQSitemapContact Us
     

   
  All ITKnowledge
  Source Code

  Search Tips
  Advanced Search
   
  

  

[an error occurred while processing this directive]
Table of Contents


Bibliography

1  Müller, B., and Reinhardt, J.: Neural Networks: An Introduction (Springer-Verlag, Berlin: 1990).
2  Gerstein, G. L., and Mandelbrot, B.: “Random walk models for the spike activity of a single neuron.” Biophys. J., 4, 1964, 41–68.
3  Feinberg, S. E., and Hochman, H. G.: “Modal analysis of renewal models for spontaneous single neuron discharges.” Biol. Cybern., 11, 1972, 201–207.
4  Neelakanta, P. S., Mobin, M. S., Pilgreen, K., and Aldes, L.: “Markovian dichotomous model of motor unit action potential trains: EMG analysis.” Proc. 9th Annual Pittsburgh Conf. (Modelling and Simulation), (May 5–6, 1988, Pittsburgh, PA), 19, Part 4, 1735–1739.
5  Neelakanta, P. S., Mobin, M. S., Pilgreen, K., and Aldes, L.: “Resolution of power spectral analysis of events in the electromyogram: An error estimation model.” Proc. 9th Annual Pittsburgh Conf. (Modelling and Simulation), (May 5–6, 1988, Pittsburgh, PA), 19, Part 5, 2421–2425.
6  Sampath, G., and Srinivasan, S. K.: Stochastical Models for Spike Trains of Single Neurons (Springer-Verlag, Berlin: 1977).
7  McCulloch, W. W., and Pitts, W.: “A logical calculus of the ideas imminent in nervous activity.” Bull. Math. Biophys., 5, 1943, 115–133.
8  MacGregor, R. J.: Neural and Brain Modeling (Academic Press, San Diego, CA: 1987), 13–33, 138–139, 140–143.
9  Wiener, N.: Cybernetics: Control and Communication in the Animal and the Machine (MIT Press, Cambridge, MA: 1948 & 1961).
10  Gabor, D.: “Theory of communication.” J. Inst. Electr. Eng., 93, 1946, 429–457.
11  Griffith, J. S.: “A field theory of neural nets I.” Bull. Math. Biophys., 25, 1963, 11–120.
12  Griffith, J. S.: “A field theory of neural nets II.” Bull. Math. Biophys., 27, 1965, 187–195.
13  Griffith, J. S.: “Modelling neural networks by aggregates of interacting spins.” Proc. R. Soc. London, A295, 1966, 350–354.
14  Griffith, J. S.: Mathematical Neurobiology (Academic Press, New York: 1971).
15  De Groff, D., Neelakanta, P. S., Sudhakar, R., and Medina, F.: “Liquid-crystal model of neural networks.” Complex Syst., 7, 1993, 43–57.
16  Neelakanta, P. S., Sudhakar, R., and De Groff, D.: “Langevin machine: A neural network based on stochastically justifiable sigmoidal function.” Bio. Cybern., 65, 1991, 331–338.
17  De Groff, D., Neelakanta, P. S., Sudhakar, R., and Medina, F.: “Collective properties of neuronal activity: Momentum flow and particle dynamics representation.” Cybernetica, XXXVI, 1993, 105–119.
18  De Groff, D., Neelakanta, P. S., Sudhakar, R., and Aalo, V.: “Stochastical aspects of neuronal dynamics: Fokker-Planck approach.” Bio. Cybern., 69, 1993, 155–164.
19  Hebb, D. O.: The Organization of Behavior (Wiley and Sons, New York: 1949).
20  Stanley-Jones, D., and Stanley-Jones, K.: Kybernetics of Natural Systems, A Study in Patterns of Control (Pergamon Press, London: 1960).
21  Uttley, A. M.: “The classification of signals in the nervous system.” Electroencephalogr. Clin. Neurophysiol., 6, 1954, 479.
22  Shannon, C. E.: “Synthesis of two-terminal switching circuits.” Bell Syst. Tech. J., 28, 1949, 656–715.
23  Walter, W. G.: The Living Brain (Duckworth, London: 1953).
24  Ashby, W. R.: An Introduction to Cybernetics (Chapman and Hall, London: 1956).
25  George, F. H.: Cybernetics and Biology (W. H. Freeman and Co., San Francisco: 1965).
26  Arbib, M. A.: Brains, Machines and Mathematics (McGraw-Hill Book Co., New York: 1964).
27  Kohonen, T.: Self-organization and Associative Memory (Springer-Verlag, Berlin: 1989).
28  Hodgkin, A. L., and Huxley, A. F.: “A quantitative description of membrane current and its application to conduction and excitation in nerve.” J. Physiol. (London), 117, 1952, 500–544.
29  Agnati, L. F., Bjelke, B., and Fuxe, K.: “Volume transmission in the brain.” Am. Scientist, 80, 1992, 362–373.
30  Turing, A. M.: “On computable numbers, with an application to the Entscheidungs problem.” Proc. London. Math. Soc. Ser. 2, 42, 1937, 230–265.
31  Hopfield, J. J.: “Neural networks and physical systems with emergent collective computational abilities.” Proc. Natl. Acad. Sci. U.S.A., 79, 1982, 2554–2558.
32  Cragg, B. G., and Temperley, H. N. V.: “The organization of neurons: A cooperative analogy.” Electroencephalogr. Clin. Neurophysiol., 6, 1954, 85–92.
33  Little, W. A.: “The existence of persistent states in the brain.” Math. Biosci., 19, 1974, 101–120.
34  Hopfield, J.J., and Tank, D.W.: “'Neural' computation of decision in optimization problems.” Biol. Cybern., 52, 1985, 1–12.
35  Ising, E.: Ph.D. dissertation, Z. Phys., 31, 1925, 253.
36  Hopfield, J. J.: “Neurons with graded response have collective properties like those of two-state neurons.” Proc. Natl. Acad. Sci. U.S.A., 81, 1984, 3088–3092.
37  Thompson, R. S., and Gibson, W. G.: “Neural model with probabilistic firing behavior. I. General considerations.” Math. Biosci., 56, 1981a, 239–253.
38  Peretto, P.“Collective properties of neural networks: A statistical physics approach.” Biol. Cybern., 50, 1984, 51–62.
39  Rosenblatt, F.: “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychol. Rev., 65, 1958, 42 & 99.
40  Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms (Spartan Books, Washington., D. C.: 1961).
41  Anderson, J. A.: “Cognitive and psychological computation with neural model.” IEEE Trans. Syst. Man Cybern., SCM-13, 1983, 799–815.
42  Beurle, R. L.: “Properties of a mass of cells capable of regenerating pulses.” Philos. Trans. R. Soc. London Ser. A, 240, 1956, 55–94.
43  Wilson, H.R., and Cowan, J.D.: A mathematical theory of the functional dynamics of cortical thalmic nervous tissue.” Biol. Cybern., 3, 1973, 55–80.
44  Wilson, H. R., and Cowan, J. D.: “Excitatory and inhibitory interactions in localized populations of model neurons.” Biophys. J., 12, 1972, 1–24.
45  Oguztoreli, M. N.: “On the activities in a continuous neural network.” Biol. Cybern., 18, 1975, 41–48.
46  Kawahara, T., Katayama, K., and Nogawa, T.: “Nonlinear equations of reaction-diffusion type for neural populations.” Biol. Cybern., 48, 1983, 19–25.
47  Ventriglia, F.: “Propagation of excitation in a model of neuronal systems.” Biol. Cybern., 1978, 30, 75–79.
48  Kung, S. Y.: Digital Neural Networks (Prentice Hall, Englewood Cliffs, NJ: 1993).
49  Bergstrom, R. M., and Nevalinna, O.: “An entropy model of primitive neural systems.” Int. J. Neurosci., 4, 1972, 171–173.
50  Takatsuji, M.: “An information-theoretical approach to a system of interacting elements.” Biol. Cybern., 17, 1975, 207–210.
51  Hinton, G. E., Sejnowski, T. J., and Ackley, D. H.: “Boltzmann machines: Constraint satisfaction networks that learn.” Tech. Rep. SMU-CS-84-119 (Carnegie-Mellon University, Pittsburgh, PA: 1984).
52  Aarts, E., and Korst, J.: Simulated Annealing and Boltzmann Machines (John Wiley and Sons, Chichester: 1989).
53  Szu, H., and Hardey, R.: “Fast simulated annealing.” Phys. Lett. A., 122, 1987, 157–162.
54  Akiyama, Y., Yamashita, A., Kajiura, M., and Aiso, H.: “Combinatorial optimization with Gaussian machines.” Proc. Int. Joint Conf. Neural Networks (June 18–22, 1990, Washington, D. C.), I 1533–1539.
55  Geman, S, and Geman, D.: “Stochastic relaxation, Gibbs distributions and Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell., 6, 1984, 721–741.
56  Jeong, H., and Park, J. H.: “Lower bounds of annealing schedule for Boltzmann and Cauchy machines.” Proc. Int. Joint Conf. Neural Networks (June 18–22, 1990, Washington, D. C.), I 581-I 586.
57  Ackley, D. H., Hinton, G. E., and Sejnowski, T. J.: “A learning algorithm for Boltzmann machines.” Cognit. Sci., 9, 1985, 147.
58  Liou, C. Y., and Lin, S. L.: “The other variant Boltzmann machine.” Proc. Joint Conf. Neural Networks (June 18–22, 1990, Washington, D. C.), 1449–454.
59  Feldman, J. A., and Ballard, D. H.: “Connectionist models and their properties.” Cognit. Sci., 6, 1982, 205–254.
60  Livesey, M.: “Clamping in Boltzmann machines.” IEEE Trans. Neural Networks, 2, 1991, 143–148.
61  Györgi, G., and Tishby, N.: “Statistical Theory of learning a rule.” Proc. Stat. Phys 17 Workshop on Neural Networks and Spin Glasses (August 8–11, 1989, Porto Alegre, Brazil). (Eds. Theumann, W. K., and R. K”berle) (World Scientific, Singapore: 1990).
62  Personnaz, L.,Guyon, I., and Dreyfus, G.: “Collective computational properties of neural networks: New learning mechanisms.” Phys. Rev. A, 34, 1989, 4303.
63  Kanter, I., and Sompolinsky, H: “Associative recall of memories without errors.” Phys. Rev. A, 35, 1987, 380.
64  Caianello, E. R.: “Outline of thought processes and thinking machines.” J. Theor. Biol., 2, 1961, 204.
65  Cowan, J. D.: “Statistical mechanics of nervous nets.” In Neural Networks (Ed. Caianello, E.R.), (Springer-Verlag, Berlin: 1968).
66  Amari, S.: “A method of statistical neurodynamics.” Biol. Cybern., 14, 1974, 201–215.
67  Ingber, L.: “Statistical mechanics of neocortical interactions. I. Basic formulation.” Phys. Rev. D, 5D, 1982, 83–107.
68  Ingber, L.: “Statistical mechanics of neocortical interactions. Dynamics of synaptic modifications.” Phys. Rev. A, 8, 1983, 395–416.
69  Amit, D. J., Gutfreund, H., and Sompolinsky, H: “Spin-glass models of neural networks.” Phys. Rev., 32, 1985a, 1007–1018.
70  Toulouse, G., Dehaene, S., and Changeux, J. P.: “Spin glass model of learning by selection.” Proc. Natl. Acad. Sciences U.S.A., 83, 1986, 1695–1698.
71  Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: “Learning representations by back-propagating errors.” Nature (London), 323, 1986, 533–536.
72  Gardner, E.: “Maximum storage capacity in neural networks.” Europhys. Lett., 4, 1987, 481–485.
73  Theumann, W. K., and Koberle, R. (Eds.): “Neural networks and spin glasses.” Proc. Stat. Phys. 17 Workshop on Neural Networks and Spin Glasses (August 8–11, 1989, Porto Alegre, Brazil), (World Scientific, Singapore: 1990).
74  MacGregor, R. J., and Lewis, E. R.: Neural Modeling (Plenum Press, New York: 1977).
75  Licklider, J. C. R.: “Basic correlates of auditory stimulus.” In Stevens Handbook of Experimental Psychology (S. S. Stevens, Ed., John Wiley, New York: 1951), 985–1039.
76  Shaw, G. L., and Vasudevan, R.: “Persistent states of neural networks and the random nature of synaptic transmission.” Math. Biosci., 21, 1974, 207–218.
77  Little, W. A., and Shaw, G. L.: “Analytic study of the memory storage capacity of a neural network.” Math. Biosci., 39, 1978, 281–290.
78  Little, W. A., and Shaw, G. L.: “A statistical theory of short and long term memory.” Behav. Biol., 14, 1975, 115–133.
79  Thompson, R. S., and Gibson, W. G.: “Neural model with probabilistic firing behavior. II. One-and two neuron networks.” Math. Biosci., 56, 1981b, 255–285.
80  Toulouse, G.: “Symmetry and topology concepts for spin glasses and other glasses.” Phys. Rep., 49, 1979, 267.
81  Brown, G. H., and Wolken, J. J.: Liquid Crystals and Biological Structures (Academic Press, New York: 1979).
82  Chandrasekhar, S.: Liquid Crystals (University Press, Cambridge: 1977).
83  Wannier, G.H.: Statistical Physics (Dover Publications, New York: 1966).
84  Stornetta, W. S., and Huberman, B. A.: “An improved three-layer back propagation algorithm.” Proc. of the IEEE First Int. Conf. Neural Networks (Eds. M. Caudill, and C. Butler, SOS Printing, San Diego, CA: 1987).
85  Indira, R., Valsakumar, M. C., Murthy, K. P. N., and Ananthakrishnan, G.: “Diffusion in bistable potential: A comparative study of different methods of solution.” J. Stat. Phys., 33, 1983, 181–194.
86  Risken, R.: The Fokker-Planck Equation (Springer, Berlin: 1984).
87  Chandrasekhar, S.: “Stochastic problems in physics and astronomy.” Rev. Mod. Phys., 15, 1943, 1–89.
88  Papoulis, A.: Probability, Random Variables and Stochastic Processes (McGraw-Hill, New York: 1984) 392.
89  Valsakumar, M. C.: “Unstable state dynamics: Treatment of colored noise.” J. Stat. Phys., 39, 1985, 347–365.
90  Bulsara, A. R., Boss, R. D., and Jacobs, E. W.: “Noise effects in an electronic model of a single neuron.” Biol. Cybern., 61, 1989, 211–222.
91  Yang, X., and Shihab, A. S.: “Minimum mean square error estimation of connectivity in biological neural networks.” Biol. Cybern., 65, 1991, 171–179.
92  Yuan, J., Bhargava, A. K., and Wang, Q.: “An error correcting neural network.” Conf. Proc. IEEE Pacific Rim Conf. on Commn., Computers & Signal Processing (June 1–2, 1989, Victoria, BC, Canada) 530–533.
93  Okuda, M., Yoshida, A., and Takahashi, K.: “A Dynamical behaviour of active regions in randomly connected neural networks.” J. Theor. Biol., 48, 1974, 51–73.
94  Pear, M. R., and Weiner, J. H.: “A Generalization of Kramer's rate formula to include some anharmonic effects.” J. Chem. Phys., 98, 1978, 785–793.
95  Marinescu, N., and Nistor, R.: “Quantum features of microwave propagation in rectangular waveguide.” Z. Naturforsch., 45a, 1990, 953–957.
96  Dirac, P.: The Principles of Quantum Mechanics (Clarendon Press, Oxford: 1987).
97  Amari, S.: “On mathematical models in the theory of neural networks.” Proc. First Int. Conf. Neural Networks, 3, 1987, 3–10.
98  Abu-Mostafa, Y.S., and St. Jacques, S.: “Information capacity of the Hopfield model.” IEEE Trans. Inf., Theor., IT-31, 1985, 461–464.
99  McEliece, R. J., Posner, E. C., Rodemich, E. R., and Venkatesh, S. S.: “The capacity of the Hopfield associative memory.” IEEE Trans. Inf. Theor., IT-33, 1987, 461–482.
100  Weisbuch, G.: “Scaling laws for the attractors of the Hopfield networks.” J. Phys. Lett., 46, 1985, L623–L630.
101  Lee, K., Kothari, S. C., and Shin, D.: “Probabilistic information capacity of Hopfield associative memory.” Complex Syst., 6, 1992, 31–46.
102  Gardner, E., and Derrida, B.: “Optimal storage properties of neural network models.” J. Phys. A, 21, 1988, 257–270.
103  Gardner, E., and Derrida, B.: “The unfinished works on the optimal storage capacity of networks.” J. Phys. A, 22, 1989, 1983–1981.
104  Shannon, C. E., and Weaver, W.: The Mathematical Theory of Communication (University of Illinois Press, Urbana: 1949).
105  Bergstrom, R. M.: “An entropy model of the developing brain.” Dev. Psychobiol., 2, 1969, 139–152.
106  Uttley, A. M.: Information Transmission in the Nervous System (Academic Press, London: 1979).
107  Pfaffelhuber, E.: “Learning and information theory.” Int. Rev. Neurosci., 3, 1972, 83–88.
108  Legendy, C. R.: “On the scheme by which the human brain stores information.” Math. Biosci., 1, 1967, 555–597.
109  MacGregor, R. J.: Theoretical Mechanics of Biological Neural Networks (Academic Press Inc./Harcourt Brace Jovanovich Publishers, Boston : 1993).
110  MacGregor, R. J., and Gerstein, G. L.: “Cross-talk theory of memory capacity in neural networks.” Bio. Cybern., 65, 1991, 351–155.
111  Venikov, V. A. (Ed.): Cybernetics in Electric Power Systems (Mir Publishers, Moscow: 1978).
112  Aczel, J., and Daroczy, Z.: On Measures of Information and Their Characteristics (Academic Press, New York: 1975).
113  Kullback, S.: Information Theory and Statistics (Dover Publications, New York: 1968).
114  Lin, J.: “Divergence measures based on the Shannon entropy.” IEEE Trans. Inf. Theor., 37, 1991, 145–151.
115  Pospelov, D. A.: “Semiotic models in control systems.” In Cybernetics Today (Ed. Makarov, I. M.), (Mir Publishers, Moscow: 1984).
116  Chowdhuri, D.: Spin Glasses and Other Frustrated Systems (Princeton University Press, Princeton, (NJ): 1986).

General Reading

A  Eccles, J. C.: The Brain and the Unity of Conscious Experience (Cambridge University Press, London: 1965).
B  Fuchs, W. R.: Cybernetics for the Modern Mind (The Macmillan Co., New York: 1971).
C  George, F. H.: Cybernetics and Biology (W. H. Freeman and Co., San Francisco: 1965).
D  Mammone, R. J., and Zeevi, Y. Y.: Neural Networks : Theory and Applications (Academic Press Inc., San Diego, CA.: 1991).
E  Nicolis, G., and Prigogine, I.: Self-Organization in Nonequilibrium Systems (John Wiley & Sons, New York: 1977).
F  Norwich, K. H.: Infomation, Sensation and Perception (Academic Press Inc., San Diego, CA.: 1993).
G  Pekelis, V.: Cybernetic Medley (Mir Publishers, Moscow: 1986).
H  Rose, J. (Ed.): Progress in Cybernetics (Vol. I&II) (Gordon and Breach Science Publishers, London: 1970).


Table of Contents

Copyright © CRC Press LLC

HomeAccount InfoSubscribeLoginSearchMy ITKnowledgeFAQSitemapContact Us
Products |  Contact Us |  About Us |  Privacy  |  Ad Info  |  Home

Use of this site is subject to certain Terms & Conditions, Copyright © 1996-2000 EarthWeb Inc. All rights reserved. Reproduction in whole or in part in any form or medium without express written permission of EarthWeb is prohibited. Read EarthWeb's privacy statement.