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Books on the topic 'Stochastic neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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1

Zhou, Wuneng, Jun Yang, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47833-2.

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2

Zhu, Q. M. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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3

Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Boston, MA: Kluwer Academic, 2003.

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4

Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing III: 28-29 July 1994, San Diego, California. Bellingham, Wash: SPIE, 1994.

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5

Focus, Symposium on Learning and Adaptation in Stochastic and Statistical Systems (2001 Baden-Baden Germany). Proceedings of the Focus Symposium on Learning and Adaptation in Stochastic and Statistical Systems. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.

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6

Su-shing, Chen, Society of Photo-optical Instrumentation Engineers., and Society for Industrial and Applied Mathematics., eds. Neural and stochastic methods in image and signal processing: 20-23 July 1992, San Diego, California. Bellingham, Wash: SPIE, 1992.

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7

Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing II: 12-13 July 1993, San Diego, California. Bellingham, Wash: SPIE, 1993.

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8

R, Dougherty Edward, and Society of Photo-optical Instrumentation Engineers., eds. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. Bellingham, Wash., USA: SPIE, 1995.

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9

Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Stochastic and neural methods in signal processing, image processing, and computer vision: 24-26 July 1991, San Diego, California. Bellingham, Wash: SPIE, 1991.

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10

International Conference on Applied Stochastic Models and Data Analysis (12th : 2007 : Chania, Greece), ed. Advances in data analysis: Theory and applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Boston: Birkhäuser, 2010.

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11

Curram, Stephen. representing intelligent decision making in discrete event simulation: a stochastic neural network approach. [s.l.]: typescript, 1997.

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12

Falmagne, Jean-Claude. Knowledge Spaces: Applications in Education. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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13

Falmagne, Jean-Claude, David Eppstein, Christopher Doble, Dietrich Albert, and Xiangen Hu. Knowledge spaces: Applications in education. Heidelberg: Springer, 2013.

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14

Hangartner, Ricky Dale. Probabilistic computation in stochastic pulse neuromime networks. 1994.

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15

Yang, Jun, Wuneng Zhou, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Springer Berlin / Heidelberg, 2016.

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16

Yang, Jun, Wuneng Zhou, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Springer, 2015.

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17

Yang, Jun, Wuneng Zhou, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Springer London, Limited, 2015.

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18

Kempka, Anthony Aaron. A stochastic technique in constructive training of artificial neural networks. 1992.

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19

Ohira, Toru. A master equation approach to stochastic neurodynamics. 1993.

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20

Stochastic Models of Neural Networks (Frontiers in Artificial Intelligence and Applications, Vol. 102). IOS Press, 2004.

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21

Thathachar, M. A. L., and P. S. Sastry. Networks of Learning Automata: Techniques for Online Stochastic Optimization. Springer, 2003.

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22

Thathachar, M. A. L. Networks of Learning Automata: Techniques For Online Stochastic Optimization. Springer, 2012.

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23

Waves In Neural Media From Single Neurons To Neural Fields. Springer-Verlag New York Inc., 2013.

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24

Dynamics On and Of Complex Networks Volume 2 Modeling and Simulation in Science Engineering and Technology. Springer-Verlag New York Inc., 2013.

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25

Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
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26

Butyrskiy, Evgeniy, and Alexandr Matveev. Mathematical modeling of systems and processes. Strategy of the Future, 2022. http://dx.doi.org/10.37468/book_011222.

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The monograph considers the fundamentals of systems theory and mathematical modeling using the principles of the systems approach. In the monograph, much attention is paid to set-theoretic, dynamic, as well as aggregative and combined models. Based on the group-theoretical approach, a generalization of the theory of signals, their characteristics are considered, their classification and some theorems are carried out. A separate chapter is devoted to mathematical models of signal interaction with the propagation medium. The monograph also considers mathematical models of stochastic signal processing and control systems based on spline filtering, artificial intelligence models and neural networks. The monograph can be useful for a wide range of specialists in various fields of knowledge dealing with mathematical modeling in their research, and can also be used as a teaching aid for conducting both classroom and independent theoretical and practical classes with bachelors, masters, graduate students in the discipline "System Theory and System Analysis", "Mathematical Modeling" and "Optimal and Suboptimal Estimation of Random Processes and Systems".
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27

Falmagne, Jean-Claude, David Eppstein, Christopher Doble, Dietrich Albert, and Xiangen Hu. Knowledge Spaces: Applications in Education. Springer, 2015.

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28

Falmagne, Jean-Claude, Christopher Doble, and Dietrich Albert. Knowledge Spaces: Applications in Education. Springer, 2013.

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