A mixed-mode polynomial-type CNN for analysing brain electrical activity in epilepsy: Research Articles

In this paper, a mixed-mode polynomial-type cellular neural network (CNN) for analysing brain electrical activity in epilepsy is presented. The principles and design characteristics of this application are overviewed. The main difference of the requirements of this application compared to conventional CNN realizations is that in addition to linear, cells are also coupled with polynomial-type couplings. A mixed-mode CNN is shown to be suitable for the realization of a polynomial-type CNN in the brain electrical activity analysis application. In a mixed-mode CNN multiplication is done in analog domain, whereas the integration and storage are digital. Suitability of different integration methods for cell level realization are studied and cell and network level design of a mixed-mode CNN is described. First-, second- and third-order polynomial feedback terms are included in the cell and Heun's integration method is used. In order to reduce the cell count, the array is designed so that it can process input data that has been divided into blocks. The whole input data is stored in parallel with the cells so that all input/output operations during processing are local. Cell structure is shown along with register connections between edge cells of the network. Analog power consumption and computing speed are estimated by HSPICE simulations using 0.25 µm digital CMOS process parameters. The die area of a network with 2×72 cells with 36 layers in each was determined by drawing the layout. Copyright © 2002 John Wiley & Sons, Ltd.