Abstract
It is unknown precisely how the nervous system of invertebrates combines multiple sensory inputs to calculate more abstract quantities, e.g., combining the angle of multiple leg joints to calculate the position of the foot relative to the body. In this paper, we suggest that non-spiking interneurons (NSIs) in the nervous system could calculate such quantities and construct a neuromechanical model to support the claim. Range fractionated sensory inputs are modeled as multiple integrate-and-fire neurons. The NSI is modeled as a multi-compartment dendritic tree and one large somatic compartment. Each dendritic compartment receives synaptic input from one sensory neuron from the knee and one from the hip. Every dendritic compartment connects to the soma. The model is constructed within the Animatlab 2 software. The neural representation of the system accurately follows the true position of the foot. We also discuss motivation for future research, which includes modeling other hypothetical networks in the insect nervous system and integrating this model into task-level robot control.
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Acknowledgements
This work was supported by NSF IIS 2113028 as part of the Collaborative Research in Computational Neuroscience Program. This work was also supported by NSF DBI 2015317 as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program.
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Guie, C.K., Szczecinski, N.S. (2022). Direct Assembly and Tuning of Dynamical Neural Networks for Kinematics. In: Hunt, A., et al. Biomimetic and Biohybrid Systems. Living Machines 2022. Lecture Notes in Computer Science(), vol 13548. Springer, Cham. https://doi.org/10.1007/978-3-031-20470-8_32
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