Reverse engineering in Drosophila larvae - Modeling neural control of learned versus innate behavior

Abstract

Event Abstract Back to Event Reverse engineering in Drosophila larvae - Modeling neural control of learned versus innate behavior Evren Pamir1, 2*, Michael Schleyer3, Timo Saumweber3, Bertram Gerber3, 4, 5 and Martin P. Nawrot1, 2 1 Freie Universität, Neuroinformatics and Theoretical Neuroscience, Germany 2 Bernstein Center for Computational Neuroscience, Germany 3 Universität Leipzig, Genetics, Institute of Biology, Germany 4 Universität Magdeburg, Behaviour Genetics, Institute of Biology, Germany 5 Leibniz Institute of Neurobiology, Genetics of Learning and Memory, Germany A series of recent behavioral studies have described learned and innate behavior in Drosophila larvae under a variety of experimental conditions (reviewed in Gerber & Stocker, 2007). In order to conceptualize these findings, a qualitative circuit-level model for the regulation of this behavior has been proposed (Schleyer et al., In Press). Here we follow and extend this approach by simulating the processing of olfactory and reinforcing stimuli in a simple sensory-to-motor circuit during olfactory conditioning and during testing of naive behavior. The proposed circuitry is constrained by neuroanatomy, in particular we introduced plastic synapses between mushroom body Kenyon cells and their output neurons, receiving reinforcement signals from the subesophageal ganglion. The expression of behavior is regulated as follows in the circuitry: (i) Innate preference is driven by the net sum of innate appetitive or aversive values of sensory stimuli. (ii) Learned preferences, in contrast, are behaviorally expressed only if this yields a positive gain compared to the acute testing situation. As expected we find that our circuit-model can well reproduce the majority of behavioral observations. In addition the proposed model constitutes a possible control-architecture for a biased random walk of an artificial agent searching for food in a circular arena. We further aim at a more detailed understanding of the extracted behavioral control principles by analyzing trajectories of individual animals recorded under different experimental conditions. Acknowledgements This work was supported by the BMBF through grant 01GQ0941 within the Bernstein Focus Neuronal Basis of Learning (BFNL). E.P was funded by the DFG within the Research Training Group 1589 Sensory Computation in Neural Systems. M.S. was funded by the Studienstiftung des Deutschen Volkes, T.S. by the University of Würzburg Graduate School Life Science. References Gerber B, Stocker RF. (2007) The Drosophila Larva as a Model for Studying Chemosensation and Chemosensory Learning: A Review. Chem. Senses 32: 65–89 Schleyer M, Saumweber T, Nahrendorf W, Fischer B, von Alpen D, Pauls D, Thum A, Gerber B. A behaviour-based circuit-model of how outcome expectations organize learned behavior in larval Drosophila. Learning & Memory, In Press. Keywords: Learning, Locomotion, Olfactory classical conditioning, plasticity Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: learning and plasticity (please use "learning and plasticity" as keyword) Citation: Pamir E, Schleyer M, Saumweber T, Gerber B and Nawrot MP (2011). Reverse engineering in Drosophila larvae - Modeling neural control of learned versus innate behavior. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00194 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: 19 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Mr. Evren Pamir, Freie Universität, Neuroinformatics and Theoretical Neuroscience, Berlin, Germany, evren.pamir@lin-magdeburg.de 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 Evren Pamir Michael Schleyer Timo Saumweber Bertram Gerber Martin P Nawrot Google Evren Pamir Michael Schleyer Timo Saumweber Bertram Gerber Martin P Nawrot Google Scholar Evren Pamir Michael Schleyer Timo Saumweber Bertram Gerber Martin P Nawrot PubMed Evren Pamir Michael Schleyer Timo Saumweber Bertram Gerber Martin P Nawrot 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.