Solid State Materials have been gaining importance in recent times especially in the context of devices which can provide necessary infrastructure and flexibility for various human endeavours. In this context, microwave materials have a unique place especially in various device applications as well as in communication networks. Various technological developments are taking place in fine-tuning these materials for specific applicatio"ns and in fixed band frequencies. Though the science and technology of these materials has reached an advanced stage, systematic attempts are still lacking in bringing all available information in a single source. The present. volume is a modest attempt in this direction, though it cannot be considered to be the one that satisfies completely desired components and information required. The editors have enlisted certain articles of interest in this area, especially those dealing with measurement techniques, chapters dealing with materials like Ferrites, YIGs, Radome and high Tc superconducting materials which are of current interest. The editors are fully aware that the coverages are not comprehensive either in scope or in depth. The purpose of this volume is only to acquaint oneself of certain aspects of a fast developing field. The editors will be grateful for any comments or suggestions in this endeavour. V. R. K. MURTHY S. SUNDARAM B. VISWANATHAN Contents Preface v 1. Materials and Processes in Microwave Integrated Circuits Fabrication 1 T. Rs. Reddy 2. Materials and Technology for Microwave Integrated Circuits 30 Bharathi Bhat and Shiban K. Koul 3.

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A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs.

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One of the great intellectual challenges for the next few decades is the question of brain organization. What is the basic mechanism for storage of memory? What are the processes that serve as the interphase between the basically chemical processes of the body and the very specific and nonstatistical operations in the brain? Above all, how is concept formation achieved in the human brain? I wonder whether the spirit of the physics that will be involved in these studies will not be akin to that which moved the founders of the "rational foundation of thermodynamics". C. N. Yang! 10 The human brain is said to have roughly 10 neurons connected through about 14 10 synapses. Each neuron is itself a complex device which compares and integrates incoming electrical signals and relays a nonlinear response to other neurons. The brain certainly exceeds in complexity any system which physicists have studied in the past. Nevertheless, there do exist many analogies of the brain to simpler physical systems. We have witnessed during the last decade some surprising contributions of physics to the study of the brain. The most significant parallel between biological brains and many physical systems is that both are made of many tightly interacting components.

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Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop­ field (1982).

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