Implementation of pulse-coupled neural networks in a CNAPS environment.

Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

Finding the shortest path in the shortest time using PCNN's.

A pulse coupled neural network (PCNN) can run mazes nondeterministically (taking all possible paths) with constant time per step. Thus, when a signal emerges, it has taken the shortest path in the shortest time.

Inherent features of wavelets and pulse coupled networks.

Biologically inspired image/signal processing like the pulse coupled neural network (PCNN) and the wavelet (packet) transforms are described. The two methods are applied to two-dimensional data in order to demonstrate the features of each method, pinpoint differences as well as similarities. The inherent properties (with respect to filtering, segmentation, etc.) of the two approaches with respect to detectors for physics experiments as well as remote sensing are discussed.

Foveation by a pulse-coupled neural network.

Humans do not stare at an image, they foveate. Their eyes move about points of interest within the image collecting clues as to the content of the image. Object shape is one of the driving forces of foveation. These foveation points are generally corners and, to a lesser extent, the edges. The pulse-coupled neural network (PCNN) has the inherent ability to segment an image. The corners and edges of the PCNN segments are similar to the foveation points. Thus, it is a natural extension of PCNN technology to use it as a foveation engine. This paper will present theory and examples of foveation through the use of a PCNN, and it will also demonstrate that it can be quite useful in image recognition.

Design for a massive all-optical bidirectional associative memory: the big BAM.

An optical bidirectional associative memory offering a potential of operating up to 10(6) neurons with 10(12) interconnections is described. Except possibly for input and output all operations are optical and parallel.