Neuromorphic olfaction by Krishna C. Persaud, Santiago Marco, Agustin Gutierrez-Galvez

By Krishna C. Persaud, Santiago Marco, Agustin Gutierrez-Galvez

content material: Engineering points of olfaction / Krishna C. Persaud --
research of the coding potency of populations of olfactory receptor neurons and olfactory glomeruli / Agustín Gutiérrez-Gálvez and Santiago Marco --
Mimicking organic olfaction with very huge chemical arrays / Mara Bernabei --
the factitious moth : a neuromorphic strategy towards synthetic olfaction in robots / Vasiliki Vouloutsi ... [et al.] --
Reactive and cognitive seek options for olfactory robots / Dominique Martinez and Eduardo Martin Moraud --
functionality of a computational version of the mammalian olfactory method / Simon Benjaminsson, Pawel Herman, and Anders Lansner.

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Extra info for Neuromorphic olfaction

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Di’Natale and coworkers (1995a, 1995b) have applied the SOM technique for the discrimination of chemical patterns, where they have proposed a new classification method that generalizes the potential function method to neural implementation in an unsupervised environment. Distante and coworkers (Distante et al. 2000, 2002; Zuppa et al. 2004) have taken this concept further to introduce multiple self-­organizing maps (mSOMs) as a powerful method for classification and feature extraction. This concept of mSOM has been used to counteract the effects of drift over time, which is a common feature of chemical sensing systems.

This map has similarity with the information processing infrastructure of the nervous system.

The visualization of sampled chemicals gives information on the interrelationship between input patterns via human observation. However, it is important in an intelligent system that the ability to classify does not rely on human judgment, so producing a system that can be truly automated. There are many ways to achieve classification using pattern recognition methods. However, parametric methods of pattern recognition (classical statistical pattern recognition methods), which assume the probability density function is known or can be estimated, do not consider the many problems associated with practical applications and are rarely used in chemical analysis.

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