Cellular neural networks and cellular wave computers

T. Roska, A. Zarándy, Csaba Rekeczky

Research output: Chapter in Book/Report/Conference proceedingChapter


Current very-large-scale integration (VLSI) technologies provide for the fabrication of chips with several million transistors. With these technologies a single chip may contain one powerful digital processor, a huge memory containing millions of very simple units placed in a regular structure, and other complex functions. A powerful combination of a simple logic processor placed in a regular structure is the cellular automaton invented by John von Neumann. The cellular automaton is a highly parallel computer architecture. Although many living neural circuits resemble this architecture, the neurons do not function in a simple logical mode: they are analog “devices.” The cellular neural network architecture, invented by Chua and his graduate student Yang [1], has both the properties: the cell units are nonlinear continuoustime dynamic elements placed in a cellular array. Of course, the resulting nonlinear dynamics in space could be extremely complex. The inventors, however, showed that these networks can be designed and used for a variety of engineering purposes, while maintaining stability and keeping the dynamic range within well-designed limits. Subsequent developments have uncovered the many inherent capabilities of this architecture (IEEE conferences: CNNA-90, CNNA-92, CNNA-94, 96, 98, 00, 02; Special issues: International Journal of Circuit Theory and Applications, 1993, 1996, 1998, 2002; and IEEE Transactions on Circuits and Systems, I and II, 1993, 1999, etc.). In the circuit implementation, unlike analog computers or general neural networks, the cellular neural/nonlinear network (CNN) cells are not the ubiquitous highgain operational amplifiers. In most practical cases, they are either simple unity-gain amplifiers or simple second-or third-order simple dynamic circuits with one to two simple nonlinear components. Tractability as well as the trillion operations per second (TeraOPS) computing speed in a single chip are but some of the many attractive properties of cellular neural networks. The trade-off is in the accuracy; however, in many cases, the accuracy achieved with current technologies is enough to solve a lot of real-life problems.

Original languageEnglish
Title of host publicationFeedback, Nonlinear, and Distributed Circuits
PublisherCRC Press
ISBN (Electronic)9781420058826
ISBN (Print)1420058819, 9781420058819
Publication statusPublished - Jan 1 2009

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Cellular neural networks and cellular wave computers'. Together they form a unique fingerprint.

  • Cite this

    Roska, T., Zarándy, A., & Rekeczky, C. (2009). Cellular neural networks and cellular wave computers. In Feedback, Nonlinear, and Distributed Circuits (pp. 15-1-15-19). CRC Press.