Glomerular Latency Coding in Artificial Olfaction
Identifieur interne : 001D12 ( Main/Exploration ); précédent : 001D11; suivant : 001D13Glomerular Latency Coding in Artificial Olfaction
Auteurs : Jaber Al Yamani [Australie] ; Farid Boussaid [Australie] ; Amine Bermak [République populaire de Chine] ; Dominique Martinez [France]Source :
- Frontiers in Neuroengineering [ 1662-6443 ] ; 2012.
English descriptors
- mix :
Abstract
Sensory perception results from the way sensory information is subsequently transformed in the brain. Olfaction is a typical example in which odor representations undergo considerable changes as they pass from olfactory receptor neurons (ORNs) to second-order neurons. First, many ORNs expressing the same receptor protein yet presenting heterogeneous dose–response properties converge onto individually identifiable glomeruli. Second, onset latency of glomerular activation is believed to play a role in encoding odor quality and quantity in the context of fast information processing. Taking inspiration from the olfactory pathway, we designed a simple yet robust glomerular latency coding scheme for processing gas sensor data. The proposed bio-inspired approach was evaluated using an in-house SnO2 sensor array. Glomerular convergence was achieved by noting the possible analogy between receptor protein expressed in ORNs and metal catalyst used across the fabricated gas sensor array. Ion implantation was another technique used to account both for sensor heterogeneity and enhanced sensitivity. The response of the gas sensor array was mapped into glomerular latency patterns, whose rank order is concentration-invariant. Gas recognition was achieved by simply looking for a “match” within a library of spatio-temporal spike fingerprints. Because of its simplicity, this approach enables the integration of sensing and processing onto a single-chip.
Url:
DOI: 10.3389/fneng.2011.00018
PubMed: 22319491
PubMed Central: 3251822
Affiliations:
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wicri:level="1"><nlm:aff id="aff1"><institution>The University of Western Australia</institution><country>Crawley, WA, Australia</country></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Bermak, Amine" sort="Bermak, Amine" uniqKey="Bermak A" first="Amine" last="Bermak">Amine Bermak</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology</institution><country>Hong Kong, China</country></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>UMR 7503, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)</institution><country>CNRS, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Neuroengineering</title><idno type="eISSN">1662-6443</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>chemical sensing</term><term>electronic nose</term><term>gas sensors</term><term>glomerular convergence</term><term>latency coding</term><term>neuromorphic engineering</term><term>olfaction</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Sensory perception results from the way sensory information is subsequently transformed in the brain. Olfaction is a typical example in which odor representations undergo considerable changes as they pass from olfactory receptor neurons (ORNs) to second-order neurons. First, many ORNs expressing the same receptor protein yet presenting heterogeneous dose–response properties converge onto individually identifiable glomeruli. Second, onset latency of glomerular activation is believed to play a role in encoding odor quality and quantity in the context of fast information processing. Taking inspiration from the olfactory pathway, we designed a simple yet robust glomerular latency coding scheme for processing gas sensor data. The proposed bio-inspired approach was evaluated using an in-house SnO<sub>2</sub> sensor array. Glomerular convergence was achieved by noting the possible analogy between receptor protein expressed in ORNs and metal catalyst used across the fabricated gas sensor array. Ion implantation was another technique used to account both for sensor heterogeneity and enhanced sensitivity. The response of the gas sensor array was mapped into glomerular latency patterns, whose rank order is concentration-invariant. Gas recognition was achieved by simply looking for a “match” within a library of spatio-temporal spike fingerprints. Because of its simplicity, this approach enables the integration of sensing and processing onto a single-chip.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Abraham, N M" uniqKey="Abraham N">N. M. Abraham</name></author><author><name sortKey="Spors, H" uniqKey="Spors H">H. Spors</name></author><author><name sortKey="Carleton, A" uniqKey="Carleton A">A. Carleton</name></author><author><name sortKey="Margrie, T W" uniqKey="Margrie T">T. W. Margrie</name></author><author><name sortKey="Kuner, T" uniqKey="Kuner T">T. 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