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.

Quantum dots (QDOTs) have been widely recognized by the scientific community and the biotechnology industry, as witnessed by the exponential growth of this field in the past several years. We describe the synthesis and characterization of visible and near infrared QDots-a critical step for engineering organic molecules like proteins and peptides for building nanocomposite materials with multifunctional properties suitable for biological applications.

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.