MetaWorm: An Integrative Data-Driven Model Simulating <i>C. elegans</i> Brain, Body and Environment Interactions — part 6/6
Cleaned-text code points [73491, 81518). Bibliography omitted here; source.md includes it.
The neural network simulation was coupled with the physical body & environment simulation to achieve closed-loop interaction. Food was at the center of the simulated physical environment, acting as an attractor to the C. elegans . The food concentration was linearly distributed in the environment. The neural system received currents regarded as sensory signals. The amplitude of the currents was proportional to the gradient of food concentration. The outputs of the neural network controlled the movements of the body in the environment through a reservoir readout weight matrix (see Method 3.2). As the body moved in the physical environment, the distance between the body and the food changed, causing the food concentration gradient updated and transformed to currents injected into the soma of sensory neurons. During the interaction of the two sub-models, the simulation time step of the neural network was 5/3 ms and that of physical environment simulation was 100 ms. The two systems synchronized every 100 ms to update the sensory signals and muscle forces. 3.2 Reservoir configuration To establish the neuron- muscle connection between the neural system and physical body muscles, a reservoir computing framework was used. The neural network model acted as the non-linear reservoir. The membrane potentials of 80 motor neurons (Supplement Table S5) were linearly combined and mapped to 96 muscle forces via an 80 by 96 matrix. The r eservoir was trained using data extracted from a 10-second movement generated in the simulated physical environment. The pairs of food concentration and muscle force during the movements were used as inputs to the neural network and target outputs of the readout layer, respectively. Only the reservoir readout matrix was optimized, while the parameters in the neural network model was fixed. 4 User-friendly Interfaces The neural network model consists of ion channel, neuron, and network connection modules. The parameter and algorithm settings of each module can be modified independently. Parameters were saved in JSON files that are easy to read and modify. Code Availability The C. elegans neural network simulation was performed with the code written in Python 3.7 and employing NEURON 8.0.0 24 as a simulation engine. The custom-developed code used in this work will be made publicly available in an online repository latest upon journal publication. If you wish to obtain the code before then, please contact us. To facilitate better evaluation of this article by reviewers, we also provide the code that can be run (we will continue to optimize our code details): https://www.dropbox.com/s/ep2yv3rmlp67eex/worm- simulation-share.zip?dl=0, Please contact us if you would like to have access to the latest code before our article is published. Methods Reference 1 Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. 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