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dc.contributor.authorPearce, Timothy C.-
dc.contributor.authorKarout, Salah-
dc.contributor.authorRácz, Zoltan-
dc.contributor.authorCapurro, Alberto-
dc.contributor.authorGardner, Julian W.-
dc.contributor.authorCole, Marina-
dc.identifier.citationFrontiers in Neuroscience, 2013, 7 : 119en
dc.description.abstractWe present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.en
dc.rightsCopyright © 2013 Pearce, Karout, Rácz, Capurro, Gardner and Cole. This is an open-access article distributed under the terms of the Creative Commons Attribution License ( ) which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.en
dc.subjectinfochemical communicationen
dc.subjectmachine olfactionen
dc.subjectneuromorphic modelen
dc.subjectpheromone processingen
dc.subjectratiometric processingen
dc.subjecttransient processingen
dc.titleRapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex.en
dc.typeJournal Articleen
dc.description.versionPublisher Versionen
dc.type.subtypeJournal Article-
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineeringen
Appears in Collections:Published Articles, Dept. of Engineering

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