Neurocomputing: foundations of research
Neurocomputing: foundations of research
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Grounding knowledge in sensors: unsupervised learning for language and planning
Grounding knowledge in sensors: unsupervised learning for language and planning
Language evolution: neural homologies and neuroinformatics
Neural Networks - Special issue: Neuroinformatics
Computing with active dendrites
Neurocomputing
Temporal processing in a spiking model of the visual system
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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Language skills are dominantly implemented in one hemisphere (usually the left), with the pre-frontal areas playing a critical part (the inferior frontal area of Broca and the superior temporal area of Wernicke), but a network of additional regions in the brain, including some from the non-dominant hemisphere, are necessary for complete language functionality. This paper presents a neural architecture built on spiking neurons which implements a mechanism of associating representations of concepts in different modalities; as well as integrating sequential language input into a coherent representation/interpretation of an instruction. It follows the paradigm of temporal binding, namely synchronisation and phase locking of distributed representations in nested gamma-theta oscillations. The functionality of the architecture is presented in a set of experiments of language instructions given to a real robot.