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Data in the real world is seldom complete. Occlusions or temporally unavailable sensors often lead to situations where incomplete data is presented for analysis. Approaches to handle incomplete data have been proposed using neural networks such as fuzzy ARTMAP and back propagation. In this paper we propose a novel approach extending the unsupervised neural network based clustering technique called...
Based on some recent advances in understanding and modeling cortical processing for space and time, we propose a developmental, general purpose model for language acquisition using multiple motor areas. Through the ventral pathway, the “what” motor learns, abstracts and projects (as recurrent top-down context) information that is related to the meaning of the text. Via the dorsal pathway, the “where/how”...
The purpose of this study is to present the application of the eye-tracking technology to the research on autistic spectrum disorders (ASD) with a special interest on language impairments and text comprehension and production deficits. We present data and results obtained from a single case study research regarding an adult autistic male with a hyperlexia profile. Furthermore we offer support to the...
It is largely unknown how the brain deals with time. Hidden Markov model (HMM) has a probability based mechanism to deal with time warping, but no effective online method exists that can deal with general active temporal abstraction. By online, we mean that the agent must respond to spatial and temporal context immediately while a sensory stream flows in. By general active temporal context, we mean...
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