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

• Published in: Scientific reports Vol. 11; no. 1; p. 13737
• Main Authors: Sakamoto, Kazuma, Soh, Zu, Suzuki, Michiyo, Iino, Yuichi, Tsuji, Toshio
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/378; Activity patterns; Cell activation; Humanities and Social Sciences; Learning algorithms; Locomotion; Machine learning; Motor neurons; multidisciplinary; Neural networks; Neurons; Oscillations; Science; Science (multidisciplinary); Worms
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-92690-2

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.