Modeling fruit fly olfactory receptor neuron-responses

Abstract

Event Abstract Back to Event Modeling fruit fly olfactory receptor neuron-responses Johannes Nehrkorn1, 2, 3, 4, 5*, Ayse Yarali3, 5, Tuba Oguz3, 4, Hiromu Tanimoto3 and Andreas V. Herz1, 2, 4 1 Ludwig-Maximilians-Universität München, Department of Biology II, Germany 2 Bernstein Center for Computational Neuroscience Munich, Germany 3 Max Planck Institute of Neurobiology, Germany 4 Graduate School of Systemic Neurosciences, Germany 5 These authors contributed equally, Germany Olfaction is critical for fundamental survival skills such as finding food, mate and shelter. Insect olfactory systems are ideally suited to study the neural basis of olfaction since they follow the same design principles and fulfill the same behavioral functions as mammalian olfactory systems but consist of much fewer neurons. In addition, mathematical models of these relatively simple neural systems can be readily implemented in technical devices. Here, we present a mathematical model to describe the response properties of fruit fly olfactory receptor neurons (ORNs). These first-order olfactory neurons have been repeatedly modeled in the past. However, all previous models ignored the influence of odor concentration, despite its pronounced effect on the “meaning” of an odor as well as its importance in tracking odor trails. To fill this gap, our model includes both odor-identity and odor-concentration. Hallem and Carlson (2006) have characterized the responses of 24 fruit fly ORN types to 19 odors across four concentrations. We take these data to fit a sigmoidal odor concentration-ORN response curve for each odor-ORN pair. Based on the parameter distribution of these sample odor-ORN pairs, we then implement a larger model consisting of 24 ORNs and 110 odors. We directly compare the predictions of this model to the corresponding large dataset of Hallem and Carlson (2006) at the single odor concentration they used. The model captures salient features of the data in terms of ORN odor tuning-widths and perceptive distances between the odors. Furthermore, we find the combinatorial odor codes to be expanding with increasing odor concentration, mimicking functional imaging data (e.g.,Root et al. 2007). In addition to fitting well with experimental data and successfully encoding odor-concentration as well as odor-identity, the model is described by only few parameters. It can thus serve as a realistic, reliable and simple input layer to models of the insect olfactory pathway attempting to explain, e.g., learning of odor-identity and -concentration or effects of odor mixtures. Acknowledgements This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) through the Bernstein Focus Neural Basis of Learning (H.T. and A.V.M.H.). Keywords: Drosophila, Olfaction Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012. Presentation Type: Poster Topic: Sensory processing and perception Citation: Nehrkorn J, Yarali A, Oguz T, Tanimoto H and Herz AV (2012). Modeling fruit fly olfactory receptor neuron-responses. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00251 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 May 2012; Published Online: 12 Sep 2012. * Correspondence: Mr. Johannes Nehrkorn, Ludwig-Maximilians-Universität München, Department of Biology II, Planegg-Martinsried, Germany, johannes.nehrkorn@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Johannes Nehrkorn Ayse Yarali Tuba Oguz Hiromu Tanimoto Andreas V Herz Google Johannes Nehrkorn Ayse Yarali Tuba Oguz Hiromu Tanimoto Andreas V Herz Google Scholar Johannes Nehrkorn Ayse Yarali Tuba Oguz Hiromu Tanimoto Andreas V Herz PubMed Johannes Nehrkorn Ayse Yarali Tuba Oguz Hiromu Tanimoto Andreas V Herz Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.