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Self-organized recurrent neural learning for language processing (ORGANIC)
Start date: Apr 1, 2009, End date: Mar 31, 2012 PROJECT  FINISHED 

The human brain is an unrivalled "engine" for speech processing and language understanding. It integrates a large variety of learning, adaptation, optimization and self-stabilization mechanisms across manydynamically interacting levels of processing. The result of this highly entwined mesh of processes is supreme robustness, efficiency, and versatility. ORGANIC adopts principles of cortical architectureand self-organizing neurodynamics for the design of a new type of cognitive architectures for speech/language tasks.The neurodynamical models will be grounded in the paradigm of Reservoir Computing is a biologically inspired perspective on how arbitrary computations can be learnt and performed in artificial neural networks which are -- like their biological rolemodels -- large, randomly grown, highly nonlinear and eminently adaptive.R\\&D activities in Organic will result in* a much deeper theoretical understanding of how very complex computations, especially those related to language processing, can be robustly and adaptively performed in neurodynamical systems,* a publicly available ``Engine'' of programming tools which conforms to recent interface standards for parallel neural system simulations, together with a reference collection of large real-life benchmark datasets,* prototype implementations of large-vocabulary online speech recognizers and handwriting recognition solutions.The consortium brings together the original pioneers in reservoir computing, leading researchers in cortical architectures for speech and language processing, speech recognition technology and anindustrial partner at the frontier of automated text recognition.
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