Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers

Abstract

Event Abstract Back to Event Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers Daisuke Miyamoto1*, Tomoki Kazawa2, Stephan Haupt2, Masashi Tabuchi3, Tomoaki Mori1, Kei Nakatani4 and Ryohei Kanzaki2 1 Graduate School of Information Science and Technology, The University of Tokyo , Japan 2 Research Center for Advanced Science and Technology, The University of Tokyo , Japan 3 Graduate School of Engineering, The University of Tokyo , Japan 4 Graduate School of Life and Environmental Science, Tsukuba University , Japan The importance of local dendritic activicty in information processing of single neurons has been generally recognized. Constructing multi-compartment simulation models that also reflect the distribution of active conductances in real neurons is the one of the big challenges to achieve realistic modeling due to the paucity of experimental data. As a remedy to this problem, we propose an estimation method combining suitable electrical stimulation and massively parallel computation using genetic algorithms. We applied this method to projection neurons in antennal lobes of silkmoth (Bombyx mori) as the first model neurons. We obtained morphological tree models containing about 4,000 cylindrical compartments from confocal microscopy images. We assumed the presence of known insect K+, Na+ and Ca2+ channels and used candidate distribution models to estimate the corresponding conductances. Several types of electrical stimuli (current ramps, optionally with added sine waves of different frequencies, and voltage clamp pulses) stimuli, were evaluated for their efficiency in providing multi-compartment models reproducing the electrophysiological data. Such estimation methods need to run multi-compartment simulations many times. To meet the computational demands of the method, we used the Real Coded Genetics Algorithm (RCGA) for parallel efficiency and MPI/OpenMP hybrid implementations on the K computer which has 640,000 SPARC64VIIIfx CPU cores and on the RIKEN Integrated Cluster of Clusters (RICC).For estimating the position of a high-conductance area in an axon, injecting a current ramp with added sine wave showed good performance even after a small number of generations of the genetic algorithm (Fig. 1). This could be related to the frequency transmission characteristics of the axon. With 32,768 genes and 200 generations per estimation, we achieved parallel efficiency as high as p=0.99987 comparing the 1,024 cores and 8,192 cores cases (Fig. 2). We showed here that 1) RCGA is suitable for estimating biophysical properties for realistic multi-compartment models with high efficiency in massively parallel computation and that 2) suitable choice of stimulation paradigms can improve the efficiency of estimation. The interaction between experimental data simulation and parameter estimation can be a useful tool to improve multi-compartment models. Figure 1 Keywords: computational neuroscience, Biophysical methods, neuron models, supercomputer simulation, genetic algorithms Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Miyamoto D, Kazawa T, Haupt S, Tabuchi M, Mori T, Nakatani K and Kanzaki R (2014). Estimation method for biophysical properties of insect neurons in the combination of suitable stimulation and multi-compartment simulation with supercomputers. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00011 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: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Daisuke Miyamoto, Graduate School of Information Science and Technology, The University of Tokyo, Meguro-ku, Japan, miyamoto@brain.imi.i.u-tokyo.ac.jp 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 Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Google Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Google Scholar Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki PubMed Daisuke Miyamoto Tomoki Kazawa Stephan Haupt Masashi Tabuchi Tomoaki Mori Kei Nakatani Ryohei Kanzaki Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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