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Erschienen in: Neural Processing Letters 1/2019

13.02.2018

Neuro-Skins: Dynamics, Plasticity and Effect of Neuron Type and Cell Size on Their Response

verfasst von: Abdolreza Joghataie, Mehrdad Shafiei Dizaji

Erschienen in: Neural Processing Letters | Ausgabe 1/2019

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Abstract

We are introducing a new type of membrane, called neuro-skin or neuro-membrane. It is comprised of neurons embedded in a plastic membrane. The skin is smart and adaptive and is capable of providing desirable response to inputs intelligently. This way, the neuro-skin can be considered as a new type of neural network with adaptivity and learning capabilities. However, in this paper, only the response of neuro-skins to a dynamic input is studied. The membrane is modelled by nonlinear dynamic finite elements. Each finite element is considered as a cell of the neuro-skin which has a neuron. The neuron is the intelligent nucleus of the element. So, the finite elements are called finite neuro-elements (FNEs). Each FNE receives feedback excitation from its own neuron, as well as from its neighbouring neurons. Contrary to dynamic plastic continuous neural networks previously studied by the authors, the neurons in a neuro-skin do not apply concentrated loads but they apply traction stresses to the surface of NFEs. The membrane is in fact a skin made up of intelligent cells representing both neural activity and mechanical plasticity. The effect of neuron type and cell size on the response of neuro-skins is studied. Trainability is another issue which is not discussed in this paper. We have used the terms neuro-skin and neuro-membrane interchangeably.

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Literatur
1.
Zurück zum Zitat Joghataie A, Torghabehi OO (2014) Simulating dynamic plastic continious neural networks by finite elements. IEEE Trans Neural Netw Learn Syst 25(8):1583–1587CrossRef Joghataie A, Torghabehi OO (2014) Simulating dynamic plastic continious neural networks by finite elements. IEEE Trans Neural Netw Learn Syst 25(8):1583–1587CrossRef
2.
Zurück zum Zitat Amari SI (1990) Mathematical foundations of neurocomputing. Proc IEEE 78:1443–1463CrossRef Amari SI (1990) Mathematical foundations of neurocomputing. Proc IEEE 78:1443–1463CrossRef
3.
Zurück zum Zitat Arora JS (2004) Introduction to optimum design, 2nd edn. Elsevier Academic Press, San Diego Arora JS (2004) Introduction to optimum design, 2nd edn. Elsevier Academic Press, San Diego
4.
Zurück zum Zitat Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13(5):815MathSciNetCrossRefMATH Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13(5):815MathSciNetCrossRefMATH
5.
Zurück zum Zitat Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two state neurons. Proc Nat Acad Sci 81:3088–3092CrossRefMATH Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two state neurons. Proc Nat Acad Sci 81:3088–3092CrossRefMATH
6.
Zurück zum Zitat Hornik K, Stinhcombe M, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366CrossRefMATH Hornik K, Stinhcombe M, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366CrossRefMATH
7.
Zurück zum Zitat Guez A, Protopopsecu V, Barhen J (1988) On the stability, storage capacity, and design of nonlinear continuous neural networks. IEEE Trans Syst Man Cybern 18(1):80–87CrossRef Guez A, Protopopsecu V, Barhen J (1988) On the stability, storage capacity, and design of nonlinear continuous neural networks. IEEE Trans Syst Man Cybern 18(1):80–87CrossRef
8.
Zurück zum Zitat Chen ZY, Xu ZB (1994) Stability analysis on a class of nonlinear continuous neural networks. In: Proceedings of the IEEE international conference on computing intelligence, IEEE world congress computer intelligence, pp 1022–1027 Chen ZY, Xu ZB (1994) Stability analysis on a class of nonlinear continuous neural networks. In: Proceedings of the IEEE international conference on computing intelligence, IEEE world congress computer intelligence, pp 1022–1027
9.
Zurück zum Zitat Fromion V(2000) Lipschitz continuous neural networks on Lp. In: Proceedings of the 39th IEEE Conference on Decision Control, pp 3528–3533 Fromion V(2000) Lipschitz continuous neural networks on Lp. In: Proceedings of the 39th IEEE Conference on Decision Control, pp 3528–3533
10.
Zurück zum Zitat Zurada JM, Kang MJ (1991) Numerical modeling of continuous-time fully coupled neural networks. In: Proceedings of the IEEE international joint conference on neural network, pp 1924–1929 Zurada JM, Kang MJ (1991) Numerical modeling of continuous-time fully coupled neural networks. In: Proceedings of the IEEE international joint conference on neural network, pp 1924–1929
11.
Zurück zum Zitat Draye JPS, Pavisic DA, Cheron GA, Libert GA (1996) Dynamic recurrent neural networks: a dynamical analysis. IEEE Trans Syst Man Cybern B Cybern 26(5):692–706CrossRef Draye JPS, Pavisic DA, Cheron GA, Libert GA (1996) Dynamic recurrent neural networks: a dynamical analysis. IEEE Trans Syst Man Cybern B Cybern 26(5):692–706CrossRef
12.
Zurück zum Zitat Sinha NK, Gupta MM, Rao DH (2000) Dynamic neural networks: an overview. In: Proceedings of the IEEE international conference on industrial technology, pp 491–496 Sinha NK, Gupta MM, Rao DH (2000) Dynamic neural networks: an overview. In: Proceedings of the IEEE international conference on industrial technology, pp 491–496
13.
Zurück zum Zitat Ruan J, Li L, Lin W (2001) Dynamics of some neural network models with delay. Phys Rev 63(5,):051906-1–051906-11 Ruan J, Li L, Lin W (2001) Dynamics of some neural network models with delay. Phys Rev 63(5,):051906-1–051906-11
14.
Zurück zum Zitat Takahashi YK, Kori H, Masuda N (2009) Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity. Phys Rev E 79:051904-1–051904-10MathSciNetCrossRef Takahashi YK, Kori H, Masuda N (2009) Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity. Phys Rev E 79:051904-1–051904-10MathSciNetCrossRef
15.
Zurück zum Zitat Liao X, Xia Q, Qian Y, Zhang L, Hu G, Mi Y (2011) Pattern formation in oscillatory complex networks consisting of excitable nodes. Phys Rev 83:056204-1–056204-12 Liao X, Xia Q, Qian Y, Zhang L, Hu G, Mi Y (2011) Pattern formation in oscillatory complex networks consisting of excitable nodes. Phys Rev 83:056204-1–056204-12
16.
Zurück zum Zitat Gao Y, Wang J (2011) Oscillation propagation in neural networks with different topologies. Phys Rev 83:031909-1–031909-8MathSciNet Gao Y, Wang J (2011) Oscillation propagation in neural networks with different topologies. Phys Rev 83:031909-1–031909-8MathSciNet
17.
Zurück zum Zitat Xu G, Littlefair G, Penson R, Callan R (1999) Application of FE-based neural networks to dynamic problems. In: Proceedings of the 6th ICONIP, vol 3, pp 1039–1044 Xu G, Littlefair G, Penson R, Callan R (1999) Application of FE-based neural networks to dynamic problems. In: Proceedings of the 6th ICONIP, vol 3, pp 1039–1044
18.
Zurück zum Zitat Ramuhalli P, Udpa L, Udpa SS (2005) Finite-element neural networks for solving differential equations. IEEE Trans Neural Netw 16(6):1381–1392CrossRef Ramuhalli P, Udpa L, Udpa SS (2005) Finite-element neural networks for solving differential equations. IEEE Trans Neural Netw 16(6):1381–1392CrossRef
19.
Zurück zum Zitat Joghataie A, Farrokh MJ (2008) Dynamic analysis of nonlinear frames by Prandtl neural networks. J Eng Mech 134(11):961–969CrossRef Joghataie A, Farrokh MJ (2008) Dynamic analysis of nonlinear frames by Prandtl neural networks. J Eng Mech 134(11):961–969CrossRef
20.
Zurück zum Zitat Chatzinakos, C, Tsouros C, Kofidis N, Margaris A (2008) A mutual information-based method for the estimation of the dimensions of chaotic dynamical systems using neural networks. In: Proceedings of the IAPR workshop cognitive information process, pp 148–152 Chatzinakos, C, Tsouros C, Kofidis N, Margaris A (2008) A mutual information-based method for the estimation of the dimensions of chaotic dynamical systems using neural networks. In: Proceedings of the IAPR workshop cognitive information process, pp 148–152
22.
23.
Zurück zum Zitat Ding X, Cao J, Alsaedi A, Alsaadi F, Hayat T (2017) Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions. Neural Netw 90:42–55CrossRef Ding X, Cao J, Alsaedi A, Alsaadi F, Hayat T (2017) Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions. Neural Netw 90:42–55CrossRef
24.
Zurück zum Zitat Cao J, Li R (2017) Fixed-time synchronization of delayed memristor-based recurrent neural networks. Sci China Inf Sci 60(3):032201MathSciNetCrossRef Cao J, Li R (2017) Fixed-time synchronization of delayed memristor-based recurrent neural networks. Sci China Inf Sci 60(3):032201MathSciNetCrossRef
25.
Zurück zum Zitat Bao H, Park JH, Cao J (2016) Synchronization of fractional-order complex-valued neural networks with time delay. Neural Netw 81:16–28CrossRefMATH Bao H, Park JH, Cao J (2016) Synchronization of fractional-order complex-valued neural networks with time delay. Neural Netw 81:16–28CrossRefMATH
26.
Zurück zum Zitat Gong W, Liang J, Zhang C, Cao J (2016) Nonlinear measure approach for the stability analysis of complex-valued neural networks. Neural Process Lett 44(2):539–554CrossRef Gong W, Liang J, Zhang C, Cao J (2016) Nonlinear measure approach for the stability analysis of complex-valued neural networks. Neural Process Lett 44(2):539–554CrossRef
27.
Zurück zum Zitat Bao H, Park JH, Cao J (2016) Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay. IEEE Trans Neural Netw Learn Syst 27(1):190–201MathSciNetCrossRef Bao H, Park JH, Cao J (2016) Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay. IEEE Trans Neural Netw Learn Syst 27(1):190–201MathSciNetCrossRef
28.
Zurück zum Zitat Bao H, Park JH, Cao J (2015) Adaptive synchronization of fractional-order memristor-based neural networks with time delay. Nonlinear Dyn 82(3):1343–1354MathSciNetCrossRefMATH Bao H, Park JH, Cao J (2015) Adaptive synchronization of fractional-order memristor-based neural networks with time delay. Nonlinear Dyn 82(3):1343–1354MathSciNetCrossRefMATH
29.
Zurück zum Zitat Song C, Cao J (2014) Dynamics in fractional-order neural networks. Neurocomputing 142:494–498CrossRef Song C, Cao J (2014) Dynamics in fractional-order neural networks. Neurocomputing 142:494–498CrossRef
30.
Zurück zum Zitat Joghataie A, Dizaji MS (2016) Neuroplasticity in dynamic neural networks comprised of neurons attached to adaptive base plate. Neural Netwo 75:77–83CrossRef Joghataie A, Dizaji MS (2016) Neuroplasticity in dynamic neural networks comprised of neurons attached to adaptive base plate. Neural Netwo 75:77–83CrossRef
31.
32.
Zurück zum Zitat Bathe KJ (1996) Finite element procedure. Prentice-Hall, Upper Saddle River Bathe KJ (1996) Finite element procedure. Prentice-Hall, Upper Saddle River
33.
Zurück zum Zitat Dunne F, Petrinic N (2006) Introduction to computational plasticity. Oxford University Press, New YorkMATH Dunne F, Petrinic N (2006) Introduction to computational plasticity. Oxford University Press, New YorkMATH
34.
Zurück zum Zitat Chopra AK (2001) Dynamics of structures-theory and application to earthquake engineering, 2nd edn. Prentice-Hall, Upper Saddle River Chopra AK (2001) Dynamics of structures-theory and application to earthquake engineering, 2nd edn. Prentice-Hall, Upper Saddle River
39.
Zurück zum Zitat Joghataie A, Dizaji MS (2009) Nonlinear analysis of concrete gravity dams by neural networks. In: Proceedings of the world congress on engineering. International Association of Engineers (IAENG), Newsood Limited, Hong Kong, pp 1022–1027 Joghataie A, Dizaji MS (2009) Nonlinear analysis of concrete gravity dams by neural networks. In: Proceedings of the world congress on engineering. International Association of Engineers (IAENG), Newsood Limited, Hong Kong, pp 1022–1027
40.
Metadaten
Titel
Neuro-Skins: Dynamics, Plasticity and Effect of Neuron Type and Cell Size on Their Response
verfasst von
Abdolreza Joghataie
Mehrdad Shafiei Dizaji
Publikationsdatum
13.02.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9795-7

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