The brain circuits of the central nervous system are formed by neurons and synapses endowed with complex dynamical properties. However, the traditional architectures of computational systems, like artificial neuronal networks, are based on connectivity rules while making use of very simplified neurons. Moreover while brain circuits operate through discontinuous signal called spikes organized in complex sequences, theoretical analysis usually deals with continuous signals. To understand circuit computations a different approach is needed: to elaborate realistic spiking networks and use them, together with experimental recordings of network activity, to investigate the theoretical basis of central network computation. As a benchmark we will use the cerebellar circuit. The cerebellum is supposed to compare expected and actual activity patterns and to reveal their congruence with respect to stored memories. By these means, the cerebellum takes part to control loops regulating movement and cognition. Experimental evidence has revealed that cerebellar circuits can dynamically regulate their activity on the millisecond time scale and operate complex spatio-temporal transformation of signals through non-linear neuronal responses. Moreover, synaptic connections can be fine-tuned by distributed forms of synaptic plasticity, the correlate of memory in neural circuits. In this project, we will develop specific chips and imaging techniques to perform neurophysiological recordings from multiple neurons in the cerebellar network. Based on the data, we will develop the first realistic real-time model of the cerebellum and connect it to robotic systems to evaluate circuit functioning under closed-loop conditions. The data deriving from recordings, large-scale simulations and robots will be used to explain circuit functioning through the "adaptable filter theory". REALNET will thus provide a radically new view on computation in central brain circuits laying the basis for new technological applications in sensori-motor control and cognitive systems.