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Recruitment Learning
This book presents a fascinating and self-contained account of "recruitment learning", a model and theory of fast learning in the neocortex. In contrast to the more common attractor network paradigm for long- and short-term memory, recruitment learning focuses on one-shot learning or "chunking" of arbitrary feature conjunctions that co-occur in single presentations. The book starts with a comprehensive review of the historic background of recruitment learning, putting special emphasis on the ground-breaking work of D.O. Hebb, W.A.Wickelgren, J.A.Feldman, L.G.Valiant, and L. Shastri. Afterwards a thorough mathematical analysis of the model is presented which shows that recruitment is indeed a...
Biologically Motivated Computer Vision
This book constitutes the refereed proceedings of the Second International Workshop on Biologically Motivated Computer Vision, BMCV 2002, held in Tübingen, Germany, in November 2002. The 22 revised full papers and 37 revised short papers presented together with 6 invited papers were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on neurons and features, motion, mid-level vision, recognition - from scenes to neurons, attention, robotics, and cognitive vision.
The Relevance of the Time Domain to Neural Network Models
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 su...
Sensor Based Intelligent Robots
Robotics is a highly interdisciplinary research topic, that requires integration of methods for mechanics, control engineering, signal processing, planning, gra- ics, human-computer interaction, real-time systems, applied mathematics, and software engineering to enable construction of fully operational systems. The diversity of topics needed to design, implement, and deploy such systems implies that it is almost impossible for individual teams to provide the needed critical mass for such endeavors. To facilitate interaction and progress on sensor-based intelligent robotics inter-disciplinary workshops are necessary through which - depthdiscussioncanbeusedforcrossdisseminationbetweendi?erentd...
Pattern Recognition by Self-organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent a...
Cognitive Science
The Mind and Brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces. Thus, an internal neurocognitive representation concept consists of a dynamical process which filters out statistical prototypes from the sensorial information in terms of coherent and adaptive n-dimensional vector fields. These prototypes serve as a basis for dynamic, probabilistic predictions or probabilistic hypotheses on prospective new data (see the recently introduced approach of "predictive coding" in neurophilosophy). Furthermore, the phenomenon o...
Computer Analysis of Images and Patterns
This volume presents the articles accepted for the 8th International Conference on Computer Analysis of Images and Patterns (CAIP’99), held in Ljubljana, Slovenia, 1{3 September 1999. The CAIP series of conferences started 14 years ago in Berlin. The series served initially as a forum for meetings between sci- tists from Western and Eastern-bloc countries. Political circumstances have changed dramatically since the inception of the conference and such contacts are fortunately no longer subject to abstrade. While CAIP conferences are still rooted in Central Europe, they now attract participants from all over the world. We received 120 submissions, which went through a thorough double blind ...
Models of Neural Networks
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 ...
Mind as Machine
The development of cognitive science is one of the most remarkable and fascinating intellectual achievements of the modern era. It brings together psychology, neuroscience, artificial intelligence, computing, philosophy, linguistics, and anthropology in the project of understanding the mind by modelling its workings. Oxford University Press now presents a masterful history of cognitive science, told by one of its most eminent practitioners.
Self-Organizing Systems
Technological systems become organized by commands from outside, as when human intentions lead to the building of structures or machines. But many nat ural systems become structured by their own internal processes: these are the self organizing systems, and the emergence of order within them is a complex phe nomenon that intrigues scientists from all disciplines. Unfortunately, complexity is ill-defined. Global explanatory constructs, such as cybernetics or general sys tems theory, which were intended to cope with complexity, produced instead a grandiosity that has now, mercifully, run its course and died. Most of us have become wary of proposals for an "integrated, systems approach" to comp...
