I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A concise tutorial description of the cellular neural network (CNN) paradigm is given, along with a precise taxonomy. The CNN is defined, and the canonical equations are described. The importance of many independent input signal arrays, adaptive templates, and the multilayer capability is emphasized and motivated by examples. It is shown how simply a wave-type partial differential equation can be generated. >

The CNN paradigm

A new invention, the cellular neural network (CNN) universal machine and supercomputer, is presented. This is the first algorithmically programmable analog array computer having real-time and supercomputer power on a single chip. The CNN universal machine is described, emphasizing its programmability as well as global and distributed analog memory and logic, high throughput via electromagnetic waves, and complex cells that may be used also for simulating a broad class of PDEs. Its implementation, the CNN universal chip, is also described, along with its use of a multichip supercomputer. Other types of hardware implementations are also briefly discussed. Details of the algorithmic aspects of the new type of analogic (analog logic) algorithms, as well as the analogic software (language, compiler, machine code, etc.) are explained, along with a brief description of the available CNN workstation for implementing and simulating these new concepts. A broad range of applications is reviewed, including neuromorphic computing, programmable physics, programmable chemistry, and programmable bionics. >

The CNN universal machine: an analogic array computer

https://www.onlinemedicalimages.com/images/medical/publications/8.%20Blood%20vessel%20segmentation%20methodologies.pdf

Blood vessel segmentation methodologies in retinal images - A survey

In this paper, we present new conditions ensuring existence, uniqueness, and Global Asymptotic Stability (GAS) of the equilibrium point for a large class of neural networks. The results are applicable to both symmetric and nonsymmetric interconnection matrices and allow for the consideration of all continuous nondecreasing neuron activation functions. Such functions may be unbounded (but not necessarily surjective), may have infinite intervals with zero slope as in a piece-wise-linear model, or both. The conditions on GAS rely on the concept of Lyapunov Diagonally Stable (or Lyapunov Diagonally Semi-Stable) matrices and are proved by employing a class of Lyapunov functions of the generalized Lur'e-Postnikov type. Several classes of interconnection matrices of applicative interest are shown to satisfy our conditions for GAS. In particular, the results are applied to analyze GAS for the class of neural circuits introduced for solving linear and quadratic programming problems. In this application, the principal result here obtained is that these networks are GAS also when the constraint amplifiers are dynamical, as it happens in any practical implementation. >

New conditions for global stability of neural networks with application to linear and quadratic programming problems

The cellular neural network (CNN) paradigm is a powerful framework for analogue non-linear processing arrays placed on a regular grid. In this paper we extend the current repertoire of CNN cloning template elements (atoms) by introducing additional non-linear and delay-type characteristics. In addition, architectures with non-uniform processors and neighbourhoods (grid sizes) are introduced. With this generalization, several well-known and powerful analogue array-computing structures can be interpreted as special cases of the CNN. Moreover, we show that the CNN with these generalized cloning templates has a general programmable circuit structure (a prototype machine) with analogue macros and algorithms. the relations with the cellular automaton (CA) and the systolic array (SA) are analysed. Finally, some robust stability results and the state space structure of the dynamics are presented.

Cellular neural networks with non-linear and delay-type template elements and non-uniform grids

1. Once over lightly 2. Introduction - notations, definitions and mathematical foundation 3. Characteristics and analysis of simple CNN templates 4. Simulation of the CNN dynamics 5. Binary CNN characterization via Boolean functions 6. Uncoupled CNNs: unified theory and applications 7. Introduction to the CNN universal machine 8. Back to basics: nonlinear dynamics and complete stability 9. The CNN universal machine (CNN - UM) 10. Template design tools 11. CNNs for linear image processing 12. Coupled CNN with linear synaptic weights 13. Uncoupled standard CNNs with nonlinear synaptic weights 14. Standard CNNs with delayed synaptic weights and motion analysis 15. Visual microprocessors - analog and digital VLSI implementation of the CNN universal machine 16. CNN models in the visual pathway and the 'bionic eye' Appendix A. A CNN template library Appendix B. Using a simple multi-layer CNN analogic dynamic template and algorithm simulator (CANDY) Appendix C. A program for binary CNN template design and optimization (TEMPO).

Cellular Neural Networks and Visual Computing

Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an entire new analog computing industry. This unique undergraduate-level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Leon Chua, co-inventor of the CNN, and Tams Roska are both highly respected pioneers in the field.

/pdf/cellular-neural-networks-and-visual-computing-foundations-5ambnz0r8x.pdf

Tamás Roska

Papers

The CNN paradigm

The CNN universal machine: an analogic array computer

Cellular neural networks with non-linear and delay-type template elements and non-uniform grids

Cellular Neural Networks and Visual Computing

Cellular Neural Networks and Visual Computing: Foundations and Applications