Redundancy reduction as a strategy for unsupervised learning
Neural Computation
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The mirror system, imitation, and the evolution of language
Imitation in animals and artifacts
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
A Mathematical Theory of Communication
A Mathematical Theory of Communication
The cortical representation of speech
Journal of Cognitive Neuroscience
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Presented is a spiking neural network architecture of human language instruction recognition and robot control. The network is based on a model of a leaky Integrate-And-Fire (lIAF) spiking neurone with Active Dendrites and Dynamic Synapses (ADDS) [1,2,3]. The architecture contains several main modules associating information across different modalities: an auditory system recognising single spoken words, a visual system recognising objects of different colour and shape, motor control system for navigation and motor control and a working memory. The main focus of this presentation is the working memory module whose function is sequential processing of word from a language instruction, task and goal representation and cross-modal association of objects and actions. We test the model with a robot whose goal is to recognise and execute language instructions. The work demonstrates the potential of spiking neurons for processing spatio-temporal patterns and the experiments present spiking neural networks as a paradigm which can be applied for modelling sequence detectors at word level for robot instructions.