Conference Paper

Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions.

Conference: Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
Source: DBLP
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Available from: Xu Li, Aug 30, 2015
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    • "The KIII set contains 14 KI sets (896 1 st order ODEs), each with its KI-field that suffices to model the EEG and pulse density outputs of the components. The distributed KIII set models operations of sensory cortices in categorizing spatiotemporal patterns of EEG in response to conditioned stimuli (CS) after learning [31] [32] [33] [34] [35] [36] [37]. "
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    ABSTRACT: This paper presents an experiment to recognize early hypoxia based on EEG analyses. A chaotic neural network, the KIII model, initially designed to model olfactory neural systems is utilized for pattern classification. The experimental results show that the EEG pattern can be detected remarkably at an early stage of hypoxia for individuals.
    Neural Information Processing, 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I; 01/2006
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    ABSTRACT: The KIII model of the chaotic dynamics of the olfactory system was designed to simulate pattern classification required for odor perception. It was evaluated by simulating the patterns of action potentials and EEG waveforms observed in electrophysiological experiments. It differs from conventional artificial neural networks in relying on a landscape of chaotic attractors for its memory system and on a high-dimensional trajectory in state space for virtually instantaneous access to any low-dimensional attractor. Here we adapted this novel neural network as a diagnostic tool to classify normal and hypoxic EEGs.
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