Neural Computing, hybrid systems, cognitive modelling, intelligent agents, distributed intelligence.

Studies information processing in the brain from a mathematical and information theoretic viewpoint. Shun-ichi Amari of information geometry fame is the director.

David MacKay's group working on machine learning, information theory, neural networks and Bayesian inference.

Inter-disciplinary research in engineering, neuroscience and medicine.

Group developing nonlinear estimation approaches which rapidly adapt on-line to speaker variations and changing noise sources.

An inter-departmental center at King's College London coordinating research and teaching in neural networks. Part of NEuroNet.

Studies computations in neural systems, ranging from biological systems, to theoretical models and VLSI applications.

Fundamental issues and applications of artificial neural networks, fuzzy systems and hybrid computational intelligence systems.

Theory and applications of statistical learning theory, support vector machines and neural networks.

Bruno Olshausen's group working on coding strategies of the visual system for object recognition and scene analysis.

Neural and computational principles underlying human and animal behaviour; computational neuroscience, cognitive science and adaptive systems.

Digital signal processing and applications to Biomedical Engineering and Industrial Systems.

A consortium of Dutch neural networks research groups coordinating research into and applications of neural networks.

Studies neural computational theories of perception and action, with an emphasis on learning. Directed by Geoffrey Hinton.

To understand memory, learning and cognition through experiments and computational models, and to apply this knowledge to create brain-like artificial intelligence and neural prosthetic devices.

Maintains geographically organized list of Neural Computing research programs worldwide.

Developing theory, algorithms, and software for neural network regression and classification.

Research in systems biophysics, theoretical biology and complex systems.

Devoted to research and development addressing the challenges of understanding how humans function at the neural and cognitive levels, and solving major technological problems related to neural network implementations.

Mechanisms of human cognition and sensory-motor learning.

Primarily concerned with analyzing data in various forms - web logs, hypertext, XML, databases, signals and images

Machine learning, statistical pattern recognition, probabilistic planning, adaptive systems.

Learning about sequential and structured data, bioinformatics, document processing, natural language processing, pattern recognition.

The theory and applications of neural networks and related techniques.

Theory and methodology of neural networks; medical applications; industrial applications; neural network simulation tools; and connectionism and cognitive modeling.

Research in artificial neural networks, pattern recognition and signal processing.

Application areas include computer vision (including image processing and video), text-based information retrieval, pattern recognition and learning in data bases, and large-scale optimization using novel methods.

Neural networks for biomedical applications, sensory analysis, quality control and signal processing.

Mathematical theories of the mind, learning and memory in biological and artificial neural networks.