Abstract
The scope of this chapter is limited with the connection structures of the biologically inspired neural networks. Many biological networks are constructed with both regular and random connections between neurons. Bioinspired systems should mimic this mixed topology of biological networks, while the artificial system is still realizable. In this chapter, we focus on cellular neural networks, which is a bioinspired network with many analog realizations. Cellular neural network models are investigated under the existence of random connections additional to its regular connections.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms (Spartan Books, 1962)
J.J. Hopfield, Neural networks and physical systems with emergent collective computational properties. Proc. Nat. Acad. Sci. 79, 2554–2558 (1982)
L.O. Chua, L. Yang, Cellular neural networks: theory and applications. IEEE Trans. Circuits Syst. I 35(10), 1257–1290 (1988)
H.R. Wilson, J.D. Cowan, A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13, 55–80 (1973)
H. Hartline, F. Ratlif, Spatial summation of inhibitory in influences in the eye of limulus, and the mutual interaction of receptor units. J. Gen. Physiol. 41, 1049–1066 (1958)
F.H.L. Da Silva, A. Hoeks, H. Smits, L.H. Zetterberg, Model of brain rhythmic activity. Biol. Cybern. 15, 27–37 (1974)
W.J. Freeman, Mass Action in the Nervous System (Academic Press, New York, 1975)
M.K. Muezzinoglu, A. Vergara, R. Huerta, T. Nowotny, N. Rulkov, H.D.I. Abarbanel, A.I. Selverston, M.I. Rabinovich, Artifcial olfactory brain for mixture identifcation, in Neural Information Processing Systems (2008), pp. 1121–1128
W. Freeman, A neurobiological theory of meaning in perception. Part I: information and meaning in nonconvergent and nonlocal brain dynamics. Int. J. Bifurc. Chaos. 13(9), 2493–2511 (2003)
D. Watts, S. Strogatz, Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
S.I. Amari, Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27(2), 77–87 (1977)
L.B. Vosshall, A.M. Wong, R. Axel, An olfactory sensory map in the fly brain. Cell 102(2), 147–159 (2000)
L.O. Chua, CNN: A Paradigm for Complexity (World Scientific, Singapore, 1998)
M.E. Yalçın, J.A.K. Suykens, J. Vandewalle, Cellular Neural Networks, Multi-scroll Chaos and Synchronization (World Scientific, 2005)
S. Espejo, C. Carmona, R. Dominguez-Castro, A. Rodriguez-Vazquez, CNN universal chip in CMOS technology. Int. J. Circuit Theory Appl. 24, 93–111 (1996)
T. Roska, L. Chua, The CNN universal machine—an analogic array computer. IEEE Trans. Circuits Syst. II Analog. Digit. Signal Process. 40(3), 163–173 (1993)
A. Rodriguez-Vazquez, G. Linan-Cembrano, L. Carranza, E. Roca-Moreno, R. Carmona-Galan, F. Jimenez-Garrido, R. Dominguez-Castro, S. Meana, ACE16k The third generation of mixed-signal SIMD-CNN ACE chips toward VSoCs. IEEE Trans. Circuits Syst. I Regul. Pap. 51(5), 851–863 (2004)
Cellular Sensory Wave Computers Laboratory: Cellular Wave Computing Library. Computer and Automation Research Institute-Hungarian Academy of Sciences (2007)
P. Foldesy, L. Kek, A. Zarandy, T. Roska, G. Bartfai, Fault-tolerant design of analogic CNN templates and algorithms-part I: the binary output case. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 46(2), 312–322 (1999)
S. Xavier-de-Souza, M.E. Yalcin, J.A.K. Suykens, J. Vandewalle, Toward CNN chip-specific robustness. IEEE Trans. Circuits Syst. I Regul. Pap. 51(5), 892–902 (2004)
E. Kose, M.E. Yalcin, A new architecture for emulating CNN with template learning on FPGA, in The 16th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2018) (2018), pp. 1–4
M.E. Yalcin, A simple programmable autowave generator network for wave computing applications. IEEE Trans. Circuits Syst. II Express Briefs. 55(11), 1173–1177 (2008)
R. Yeniceri, M.E. Yalcin, Path planning on cellular nonlinear network using active wave computing technique, in Proceedings of SPIE, Bio-engineered and Bioinspired Systems IV, vol. 7365 (2009)
V. Kilic, M.E. Yalcin, An active wave computing based path finding approach for 3-D environment, in Proceedings of the IEEE International Symposium of Circuits and Systems (ISCAS 11) (2011), pp. 2165–2168
D. Watts, S. Strogatz, Collective dynamics of small-world networks. Nature 393, 440–442 (1998)
J.W. Bohland, A.A. Minai, Efficient associative memory using small-world architecture. Neurocomputing 3840, 489–496 (2001)
K.Y.N. Tsurata, Y. Zonghuang, A. Ushida, Small-world cellular neural networks for image processing applications, in Proceedings of European Conference on Circuit Theory and Design (2003), pp. 225–228
T. Ayhan, M.E. Yalcin, An application of small-world cellular neural networks on odor classification. Int. J. Bifurc. Chaos. 22(1), 1–12 (2012)
T. Ayhan, M.E. Yalcin, Randomly reconfigurable cellular neural network, in Proceedings of the 20th European Conference on Circuit Theory and Design (ECCTD11) (2011), pp. 625–628
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s), under exclusive licence to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yalçın, M.E., Ayhan, T., Yeniçeri, R. (2020). Artificial Neural Network Models. In: Reconfigurable Cellular Neural Networks and Their Applications. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-17840-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-17840-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17839-0
Online ISBN: 978-3-030-17840-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)