Peter L. Venetianer

The use of CNN models in the subcortical visual pathway

The equivalent notions of neuroanatomy and the cellular neural network (CNN) model are discussed with a view toward studying the visual system. Various mainly subcortical phenomena are studied and simple effects like directional sensitivity and length tuning are modeled. A more accurate retina model has been developed, taking into account some effects of amacrine cells. It is shown that the standard errors occurring in simple models of retinal illusions can be eliminated by using the more accurate models including delays. Lateral geniculate nucleus (LGN) effects with and without cortical feedback are modeled as well; their CNN models are simple. Simple texture detection effects and motion illusions are explained by neuromorphic CNN models. The goal is to translate known effects into CNN models and to provide a framework for further studies. >

The CNN is universal as the Turing machine

It is shown that the game of life algorithm, which is equivalent to a Turing machine, can be realized by a cellular neural network (CNN). Thus the CNN is also universal. >

On a CNN chip-prototyping system

An analogic CNN chip prototyping and development system was designed and manufactured to test and measure different VLSI implementations of the analogic CNN Universal Machine. A high level language was developed to support the design of analogic algorithms and an image capture was designed for on-chip image sensing and through CCD camera.<<ETX>>

Some novel capabilities of CNN: game of life and examples of multipath algorithms

The authors show that the game of life algorithm, which is equivalent to a Turing machine, can be realized by cellular neural networks (CNNs). They also present a multipath CNN algorithm that demonstrates the capabilities of analog/logic (analogic) software. Complex image processing tasks can be realized by programmable dual computing CNNs. A specific example of blob-counting in a gray-scale image is presented.<<ETX>>

Analog combinatorics and cellular automata-key algorithms and layout design

This paper demonstrates how certain logic and combinatorial tasks can be solved using CNNs. The most important application generalizes a shortest path algorithm to design the layout of printed circuit boards. Besides, it is shown how cellular automata can be simulated on CNN, and tasks, such as sorting, parity analysis, histogram calculation of black-and-white images, and computing minimum Hamming distance are also solved.<<ETX>>

ACE: a digital floating point CNN emulator engine

The architecture of ACE, a multiprocessor analogic cellular neural network (CNN) emulator engine consisting of 2 to 16 TMS320C40 floating point DSPs is introduced. The engine containing up to 512 Mbyte RAM (enough to store a 512/spl times/512/spl times/512 sized CNN cube) which can be controlled through its SCSI port. It can either accelerate the multilayer CNN simulator CNNM or be accessed directly from the high level, C-based analogic CNN language ACL to achieve the simulation speed of /spl sim/2.8 /spl mu/sec/cell/iteration/DSP for 3/spl times/3 linear templates.

ANALOGUE COMBINATORICS AND CELLULAR AUTOMATA—KEY ALGORITHMS AND LAY-OUT DESIGN†

This paper demonstrates how certain logic and combinatorial tasks can be solved using CNNs. A design method is proposed for solving combinatorial tasks on a CNN. It can be used to simulate cellular automata on a CNN, to prove the self-reproducing capability of the CNNUM and for sorting, histogram calculation, parity analysis and minimum Hamming distance computation. These solutions are especially useful since they can serve as subroutines of more complex CNNUM algorithms. As an important real-life application the lay-out of printed circuit boards is designed with the CNNUM at an extremely high speed.

Image compression by CNN

This paper presents a very efficient image compression method well suited to the local nature of the CNN Universal Machine. In the case of lossless image compression it outperforms the JPEG image compression standard both in terms of compression efficiency and speed. It performs especially well with radiographical images (mammograms), therefore it is suggested that it could be used as part of a CNN based mammogram analysis system.