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In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental techniques have enabled biologists to gather the data necessary to test these new theories. While we do not yet understand how the brain sees, hears or smells, we do have testable models of specific components of visual, auditory, and olfactory processing. Some of these models have been applied to help construct artificial vision and hearing systems. Similarly, our understanding of motor control has grown to the point where it has become a useful guide in the development of artificial robots. Many neuroscientists believe that we have only scratched the surface, and that a more complete understanding of biological information processing is likely to lead to technologies whose impact will propel another industrial revolution. Neural Systems: Analysis and Modeling contains the collected papers of the 1991 Conference on Analysis and Modeling of Neural Systems (AMNS), and the papers presented at the satellite symposium on compartmental modeling, held July 23-26, 1992, in San Francisco, California. The papers included, present an update of the most recent developments in quantitative analysis and modeling techniques for the study of neural systems.
Conformal invariance has been a spectacularly successful tool in advancing our understanding of the two-dimensional phase transitions found in classical systems at equilibrium. This volume sharpens our picture of the applications of conformal invariance, introducing non-local observables such as loops and interfaces before explaining how they arise in specific physical contexts. It then shows how to use conformal invariance to determine their properties. Moving on to cover key conceptual developments in conformal invariance, the book devotes much of its space to stochastic Loewner evolution (SLE), detailing SLE’s conceptual foundations as well as extensive numerical tests. The chapters then elucidate SLE’s use in geometric phase transitions such as percolation or polymer systems, paying particular attention to surface effects. As clear and accessible as it is authoritative, this publication is as suitable for non-specialist readers and graduate students alike.
Today a multinational video game developer, Sega was the first to break Nintendo's grip on the gaming industry, expanding from primarily an arcade game company to become the dominant game console manufacturer in North America. A major part of that success came from the hard work and innovation of its subsidiary, Sega of America, who in a little more than a decade wrested the majority market share from Nintendo and revolutionized how games were made. Drawing on interviews with nearly 100 Sega alumni, this book traces the development of the company, revealing previously undocumented areas of game-making history, including Sega's relationship with Tonka, the creation of its internal studios, and major breakthroughs like the Sega Channel and HEAT Network. More than 40 of the company's most influential games are explored in detail.
An argument for retaining the notion of personal property in the products we “buy” in the digital marketplace. If you buy a book at the bookstore, you own it. You can take it home, scribble in the margins, put in on the shelf, lend it to a friend, sell it at a garage sale. But is the same thing true for the ebooks or other digital goods you buy? Retailers and copyright holders argue that you don't own those purchases, you merely license them. That means your ebook vendor can delete the book from your device without warning or explanation—as Amazon deleted Orwell's 1984 from the Kindles of surprised readers several years ago. These readers thought they owned their copies of 1984. Until, it turned out, they didn't. In The End of Ownership, Aaron Perzanowski and Jason Schultz explore how notions of ownership have shifted in the digital marketplace, and make an argument for the benefits of personal property. Of course, ebooks, cloud storage, streaming, and other digital goods offer users convenience and flexibility. But, Perzanowski and Schultz warn, consumers should be aware of the tradeoffs involving user constraints, permanence, and privacy. The rights of private property are clear, but few people manage to read their end user agreements. Perzanowski and Schultz argue that introducing aspects of private property and ownership into the digital marketplace would offer both legal and economic benefits. But, most important, it would affirm our sense of self-direction and autonomy. If we own our purchases, we are free to make whatever lawful use of them we please. Technology need not constrain our freedom; it can also empower us.
Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.
High-definition is now ubiquitous in video production and High Definition Cinematography, Third Edition provides the explanations, definitions, and workflows that today's cinematographers and camera operators need to make the transition. Paul Wheeler will explain the high-definition process, suggest the best methods for filming, and help you choose the right camera and equipment for your crew with this comprehensive book. You'll also learn the different formats and when best to use them, how to create specific looks for different venues, and learn how to operate a wide variety of popular cameras.