Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

Abstract Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyz...

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Autores principales:	Kazuma Sakamoto, Zu Soh, Michiyo Suzuki, Yuichi Iino, Toshio Tsuji
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/afeab3b58d284e1685c679e958b044e1
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spelling	oai:doaj.org-article:afeab3b58d284e1685c679e958b044e12021-12-02T14:33:57ZForward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation10.1038/s41598-021-92690-22045-2322https://doaj.org/article/afeab3b58d284e1685c679e958b044e12021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92690-2https://doaj.org/toc/2045-2322Abstract Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.Kazuma SakamotoZu SohMichiyo SuzukiYuichi IinoToshio TsujiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kazuma Sakamoto Zu Soh Michiyo Suzuki Yuichi Iino Toshio Tsuji Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
description	Abstract Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.
format	article
author	Kazuma Sakamoto Zu Soh Michiyo Suzuki Yuichi Iino Toshio Tsuji
author_facet	Kazuma Sakamoto Zu Soh Michiyo Suzuki Yuichi Iino Toshio Tsuji
author_sort	Kazuma Sakamoto
title	Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
title_short	Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
title_full	Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
title_fullStr	Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
title_full_unstemmed	Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
title_sort	forward and backward locomotion patterns in c. elegans generated by a connectome-based model simulation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/afeab3b58d284e1685c679e958b044e1
work_keys_str_mv	AT kazumasakamoto forwardandbackwardlocomotionpatternsincelegansgeneratedbyaconnectomebasedmodelsimulation AT zusoh forwardandbackwardlocomotionpatternsincelegansgeneratedbyaconnectomebasedmodelsimulation AT michiyosuzuki forwardandbackwardlocomotionpatternsincelegansgeneratedbyaconnectomebasedmodelsimulation AT yuichiiino forwardandbackwardlocomotionpatternsincelegansgeneratedbyaconnectomebasedmodelsimulation AT toshiotsuji forwardandbackwardlocomotionpatternsincelegansgeneratedbyaconnectomebasedmodelsimulation
_version_	1718391152073244672

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

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