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Cognitive science is about understanding both natural and artificialintelligence, and requires interdisciplinary approaches. At the intersection of biology and human sciences, it borrowstechniques and concepts from many scientific domains - psychology,neuroscience, linguistics, philosophy, anthropology, ethology, socialscience, computer science, mathematics and theoretical physics.The RIKENBrain science institute (http://www.brain.riken.go.jp/) in Japan is one ofthe best examples of interdisciplinary institutes which have been createdin the recent years all over the world, with Departments on severalaspects of cognitive science: Understanding the Brain, Protecting theBrain,Creating the Brain (artificial intelligence, robotics), andNurturing the Brain (brain and cognitive development). Interdisciplinarylaboratories and institutes can also be found in Europe: one may mentionthe Cognitive Neuroscience Sector at SISSA, Trieste; the GATSBYComputational Neuroscience Unit at UCL, London; the EPFL's Brain and MindInstitute in Lausanne; The Cognitive Science Institute (ISC) in Lyon andthe Department of Cognitive Studies at Ecole NormaleSuperieure in Paris.

Several important topics in Cognitive Science are concerned by the field of Complex systems. Here are a few examples:

• Modeling and simulating brain functionsrequires the mathematical analysis of large interconnected neural networks.
• Brain Regions Synchronization (by Andrzej Nowak) http://shum.huji.ac.il/~sorin/report/Syncronization-of-Brain-Regions-Nowak.doc
• The study of collective behaviour, in social animals and in human societies requires similar tools.
• The analysis of data from brain imaging leads to difficult problems in artificial vision, data analysis, modeling.

An important Complex systems topics concerning Cognitive science and many other fields, is the one of learning. This name evoques different but related aspects.

• On one side, the theoretical and experimental study oflearning and adaptation, at all possible scales: at the cellular level, the network level,the agent level (analysis of human behaviour), at the collective level.
• On the other side, solving inverse problems requires the development of algorithms which can be understood as having a system learning a rule (a fonction, a task) from examples.

Learning theory, statistical (Bayesian) inference, optimal control, learning in formal and natual neural networks, supervised or unsupervised learning, reinforcement learning, are all different aspects of this same general topics. On the computer science side, the name Machine Learning denotes this general approach of learning a task from examples making use of any relevant algorithm. Machine Learning is proving an increasingly important tool in many fields, such as Machine Vision, Speech, Haptics, Brain Computer

Interfaces, Bioinformatics, and Natural Language Processing. The EU has supported specific actions related to Machine Learning. For instance, the PASCAL network of excellence of the FP6 program (see http://www.pascal-network.org/ ) was created in order to build a Europe-wide distributed Institute, whose goal is to pioneer principled methods of pattern analysis, statistical modelling and computational learning. Combination of concepts and tools taken from learning theory and

Statistical Physics have allowed to address the modelling of dynamical networks, such as neural networks, genetic networks, metabolic networks, social networks, communication networks...In such domain, one has to both, solve inverse problems (finding the network parameters from the empirical observation of the network activity), analyse and/or simulate the network dynamics, in particular when the network components (cells, proteins, human agents.) are allowed to adapt to the environment and to the behaviour of the other components.

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