Abstract
As predicted, intelligent networks with intelligent, wide multi-stage parallel processing have become a favorite in new-media applications like object-recognition and augmented reality with most recent energy efficiencies >1 Tera operations per Watt, 100-times better than conventional von-Neumann processors. Some leading companies in video- and graphics processors and in computer-aided design (CAD) tools are now introducing such products, and the Human-Brain projects enforce this long-term strategy. We see a further outgrowth of neural chips into networks. Firstly, they will appear in health monitoring for healthy living. Non-invasive measurements require time- and space-dependent models that are real-time derived from data. The size of complex neural systems becomes feasible due to further miniaturization by physical innovations like the memristor as well as by novel digital architectures. Together, that will provide effects of self-healing, a level of dependability required for large-scale distributed intelligent systems.
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
Lederman, L., Teresi, D.: The God Particle. Dell Publishing, New York (2008)
Forstner, K.: Pulsoximetrie - Stand und Entwicklung der Technik. Biomed. Tech. 33(3), 6–9 (1988)
Dickson, C.: Heart rate artifact suppression. M.Sc. Thesis, Grand Valley State University, Michigan, USA (2012)
Klofutar, A., Höfflinger, B., Neußer, S., Spaanenburg, L.: Robust QRS detection with neuroprocessing. In: Digest 2nd European Conference on Engineering and Medicine, Stuttgart, pp. 64–70 (1993)
Jo, J., Lee, Y.K., Shin, H.S.: Real-time analysis of heart rate variability for a mobile human emotion recognition system. In: Recent Advances in Electrical and Computer Engineering, pp. 162–166 (2013)
Moravec, H.: Robot–mere machine to transcendent mind. Oxford University Press, Oxford (2000)
Nijhuis, J.A.G., Höfflinger, B., Neusser, S., Siggelkow, A., Spaanenburg, L.: A VLSI implementation of a neural car collision avoidance controller. In: Proceedings of IJCNN Seattle WA, vol. 1, pp. 493–499 (1991)
Von der Malsburg, C., Schneider, W.: A neural cocktail-party processor. Biol. Cybern. 54(1), 29–40 (1986)
Roth, U., Jahnke, A., Klar, H.: Hardware requirements for spike-processing neural networks. In: Proceedings of IWANN (1995)
Hsu, J.: IBM’s new brain. IEEE Spectr. 51(11), 17–19 (2014)
Chua, L.O.: Memristor—the missing circuit element. IEEE Trans. Circ. Theor. 18(5), 507–519 (1971)
Chua, L.O.: Resistance switching memories are memristors. Appl. Phys. A 102(4), 765–783 (2011)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, S.R.: The missing memristor found. Nature 453(7191), 80–83 (2008)
Thomas, A.: Memristor-based neural networks. J. Phys. D Appl. Phys. 46(9), 93001 (2013)
Amin, M.: Towards self-healing infrastructure systems. IEEE Comput. 8(8), 44–53 (2000)
Amin, M.: North America’s electricity infrastructure—Are we ready for more perfect storms? IEEE Secur. Priv. 1(5), 19–25 (2003)
Hofmeyr, S.A., Forrest, S.: Architecture for an artificial immune system. Evol. Comput. 8(4), 443–473 (2000)
Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(1), 14–23 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Spaanenburg, L., Jansen, W.J. (2016). Networked Neural Systems. In: Höfflinger, B. (eds) CHIPS 2020 VOL. 2. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-319-22093-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-22093-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22092-5
Online ISBN: 978-3-319-22093-2
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)