Event Abstract Back to Event A spiking point of view - Is it possible to predict a neurons morphology from its electrophysiological activity? Anneke Meyer1, 2*, C. G. Galizia2 and Martin P. Nawrot1 1 Freie Universität Berlin, Germany 2 Department for Neurobiology/ Universität Konstanz, Germany Here we investigate neurons of two morphological classes, which reside in the Antennal Lobe (Al) of the honey bee: local neurons (LNs) and projection neurons (PNs). We ask, whether it is possible to conclude the neurons’ morphological class from the neurons’ electro-physiological properties. If this is the case, which combination of features of electrophysiological activity will most effectively describe the difference between LNs and PNs? We analyzed data from 80 intracellularly recorded AL neurons, 33 of which were unambiguously identified as either LN or PN. For each and every neuron we estimated a number of electro-physiological measures. We then explored clustering of neurons based on the principle components of every possible combination of these measures. Using hierarchical clustering with wards-linkage (REF) we determined which combination of measures performed best in separating identified PNs and LNs. Our analyses show that it is indeed possible to separate [class, with high certainty] LNs and PNs on the grounds of electro-physiological features only. We achieved the by far best clustering results (81% separation) based on only two PCs from a combination of six selected features, namely the CV2 (a measure of spiking irregularity), the Fano-Factor (a measure of across-trial spike count variability), the rate change (from spontaneous to stimulus response), the average response latency, the latency variation (across different stimuli), and the baseline power of the membrane potential. Many other feature combinations perform less well but still satisfyingly (70%-76% classification success). Amongst these good combinations, measures of spiking activity were more frequently represented than measures of sub-threshold activity. Neurons in the LN and PN cluster differed significantly for those features which we identified as most potent. Hence, the separation which we achieved in the principle component space can be described statistically on the level of the original features. We conclude that LNs and PNs in the honey bee AL are characterized by different electrophysiological properties, which are represented by measurable features. Still, none of these measures alone is a potent tool to predict a neuron’s morphology. The combination of several classical measures however offers a foundation to tell apart LNs from PNs with a certain probability in cases where morphological data is not at hand. Funding was received from the German Federal Ministry of Education and Research within the Bernstein Focus Neuronal Basis of Learning - Insect Inspired Robots (grant No. 01GQ0941) and from the Landesgraduiertenförderung Baden-Württemberg. Acknowledgements We thank Sabine Krofcik, Bernd Kimmerle, Randolf Menzel, Michael Schmuker, Titziano D'Albis and Jan Sölter Keywords: antennal lobe, clustering, data analysis and machne learning, Electrophysiology, Honeybee, Olfaction Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: data analysis and machine learning (please use "data analysis and machine learning" as keyword) Citation: Meyer A, Galizia CG and Nawrot MP (2011). A spiking point of view - Is it possible to predict a neurons morphology from its electrophysiological activity?. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00151 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: 22 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Dr. Anneke Meyer, Freie Universität Berlin, Berlin, Germany, anneke.meyer@uni-konstanz.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 Anneke Meyer C. G Galizia Martin P Nawrot Google Anneke Meyer C. G Galizia Martin P Nawrot Google Scholar Anneke Meyer C. G Galizia Martin P Nawrot PubMed Anneke Meyer C. G Galizia 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.