Biologically inspired approaches for artificial sensing have been extensively applied to different sensory modalities over the last decades and chemical senses have been no exception. The olfactory system, and the gustatory system to a minor extent, has been regarded as a model for the development of new artificial chemical sensing s- tems. One of the main contributions to this field was done by Persaud and Dodd in 1982 when they proposed a system based on an array of broad-selective chemical sensors coupled with a pattern recognition engine. The array aimed at mimicking the sensing strategy followed by the olfactory system where a population of bro- selective olfactory receptor neurons encodes for chemical information as patterns of activity across the neuron population. The pattern recognition engine proposed was not based on bio-inspired but on statistical methods. This influential work gave rise to a new line of research where this paradigm has been used to build chemical sensing instruments applied to a wide range of odor detection problems. More recently, some researchers have proposed to extend the biological inspiration of this system also to the processing of the sensor array signals. This has been mo- vated in part by the increasing body of knowledge available on biological olfaction, which has become in the last decade a focus of attention of the experimental neu- science community.

Many advances have been made in the last decade in the understanding of the computational principles underlying olfactory system functioning. Neuromorphic Olfaction is a collaboration among European researchers who, through NEUROCHEM (Fp7-Grant Agreement Number 216916)-a challenging and innovative European-funded project-introduce novel computing paradigms and biomimetic artifacts for chemical sensing. The implications of these findings are relevant to a wide audience, including researchers in artifical olfaction, neuroscientists, physiologists, and scientists working with chemical sensors. Developing neuromorphic olfaction from conceptual points of view to practical applications, this cross-disciplinary book examines: The biological components of vertebrate and invertebrate chemical sensing systems The early coding pathways in the biological olfactory system, showing how nonspecific receptor populations may have significant advantages in encoding odor intensity as well as odor identity The redundancy and the massive convergence of the olfactory receptor neurons to the olfactory bulb A neuromorphic approach to artificial olfaction in robots Reactive and cognitive search strategies for olfactory robots The implementation of a computational model of the mammalian olfactory system The book's primary focus is on translating aspects of olfaction into computationally practical algorithms. These algorithms can help us understand the underlying behavior of the chemical senses in biological systems. They can also be translated into practical applications, such as robotic navigation and systems for uniquely detecting chemical species in a complex background.

Biologically inspired approaches for artificial sensing have been extensively applied to different sensory modalities over the last decades and chemical senses have been no exception. The olfactory system, and the gustatory system to a minor extent, has been regarded as a model for the development of new artificial chemical sensing s- tems. One of the main contributions to this field was done by Persaud and Dodd in 1982 when they proposed a system based on an array of broad-selective chemical sensors coupled with a pattern recognition engine. The array aimed at mimicking the sensing strategy followed by the olfactory system where a population of bro- selective olfactory receptor neurons encodes for chemical information as patterns of activity across the neuron population. The pattern recognition engine proposed was not based on bio-inspired but on statistical methods. This influential work gave rise to a new line of research where this paradigm has been used to build chemical sensing instruments applied to a wide range of odor detection problems. More recently, some researchers have proposed to extend the biological inspiration of this system also to the processing of the sensor array signals. This has been mo- vated in part by the increasing body of knowledge available on biological olfaction, which has become in the last decade a focus of attention of the experimental neu- science community.

Many advances have been made in the last decade in the understanding of the computational principles underlying olfactory system functioning. Neuromorphic Olfaction is a collaboration among European researchers who, through NEUROCHEM (Fp7-Grant Agreement Number 216916)-a challenging and innovative European-funded project-introduce novel computing paradigms and biomimetic artifacts for chemical sensing. The implications of these findings are relevant to a wide audience, including researchers in artifical olfaction, neuroscientists, physiologists, and scientists working with chemical sensors. Developing neuromorphic olfaction from conceptual points of view to practical applications, this cross-disciplinary book examines: The biological components of vertebrate and invertebrate chemical sensing systems The early coding pathways in the biological olfactory system, showing how nonspecific receptor populations may have significant advantages in encoding odor intensity as well as odor identity The redundancy and the massive convergence of the olfactory receptor neurons to the olfactory bulb A neuromorphic approach to artificial olfaction in robots Reactive and cognitive search strategies for olfactory robots The implementation of a computational model of the mammalian olfactory system The book's primary focus is on translating aspects of olfaction into computationally practical algorithms. These algorithms can help us understand the underlying behavior of the chemical senses in biological systems. They can also be translated into practical applications, such as robotic navigation and systems for uniquely detecting chemical species in a complex background.