Neuromechanical Modeling of Nematode C. elegans via Modular Integration and Deep Learning

Abstract

Neural circuits within the nervous system use coordinated activities to control behavior. The mediation of neural activities by individual neuron dynamics and their integration within the nervous system represents a fundamental question in neuroscience. Computational approaches that integrate modeling of the nervous system, muscles, and the body can assist in investigating functional pathways that guide neural activities and movement. Such approaches are referred to as neuromechanical models, as they incorporate models of the nervous system and biomechanics to achieve simultaneous simulation of neural activities and behavior. Nematode Caernorhabditis elegans (C. elegans) is considered a viable framework for studying neuromechanics due to advances in the resolution of its nervous system connectomics, biomechanics, and electrophysiological recordings of neuronal activity. The availability of data allows for the construction of neuromechanical model candidates with varying scopes and modalities. In my PhD research, I proposed key methods for the identification, construction, and extension of neuromechanical models for C. elegans. In particular, I proposed the modular integration approach and its implementation, modWorm, for modeling and simulating neuromechanical model candidates. The modWorm software allows for the construction of a model as an integrated series of configurable and exchangeable modules, each describing specific biophysical processes. Using modWorm, I proposed an initial candidate for the integrated neuromechanical model of C. elegans. The model integrates the complete connectome and 7 biophysical modules, including intra- and extra-cellular neural dynamics, translation of neural dynamics to muscle dynamics, muscle dynamics to body postures, and proprioceptive feedback from the environment. The model recapitulates i) Known natural behavioral responses, such as forward and backward locomotion in response to associated neural stimuli or external forces, and ii) Transitional behaviors, such as avoidance and turns, through timed stimulus. We performed computational ablation studies on neurons to infer novel neural circuits involved in sensorimotor behaviors (e.g., touch response). Variations of the model’s modules, such as more detailed intra- and extra-cellular dynamics, connectome mappings, and optimizations of associated parameters, can delineate possiblemechanisms of locomotion and directions in which the model can be improved to fit experimental findings. For an extension of modWorm modality, I developed mod-SenseWorm to incorporate environmental stimulus during the simulation of C. elegans behavior (e.g., chemotaxis). In particular, mod-SenseWorm incorporates the dynamic translation of external stimulus into neural stimulation to achieve a closed-loop simulation between neuromechanics and the surrounding environment. The translation algorithms employed by individual neurons can be configured by setting their stimulus encoding properties (e.g., tonic, phasic) and anatomical locations in the body (e.g., anterior, posterior). We applied mod-SenseWorm to study C. elegans O2 aerotaxis behavior and showed that the proposed model, in conjunction with the simulation of an O2 environment, recapitulates empirically observed avoidance behaviors associated with increased O2 levels. Furthermore, through the analysis of simulated neural activities, we show the use case of mod-SenseWorm to infer potential functional circuits associated with chemotactic responses. Deep learning methods can assist in extending the scope of the proposed neuromechanical model by inferring the parameters of biologically detailed modules associated with empirical data. This led me to develop ElectroPhysiomeGAN (EP-GAN), a deep generative method for the estimation of biophysical neuron parameters associated with neuron models from recorded electrophysiological responses. Trained with simulation data, EP-GAN learns the translation from recorded neuron responses (e.g., membrane potential responses, steady-state currents) to biophysical model parameters associated with the detailed Hodgkin-Huxley (HH) model. Validation of EP-GAN by estimating HH-model parameters for 200 simulatednon-spiking neurons, followed by 9 experimentally recorded neurons in C. elegans, showed EP-GAN’s advantages in the accuracy of the estimated parameters and inference speed compared to existing estimation methods. Control strategies can further extend the modality of the neuromechanical model by inferring supplemental mechanisms of neural circuits associated with behavior. In particular, I have introduced a possible employment of deep reinforcement learning (DeepRL) methods to develop control strategies for both neural stimulation (neuromodulatory control) and neural connection mapping (connectome control) that are applied on top of the proposed neuromechanical model to achieve aimed behaviors. The strategies learned by DeepRL can be used to identify dynamic neuromodulatory inputs between neurons (e.g., neuropeptidic currents) and perturbations of the connection wiring map for a local neural circuit, which result in empirically observed chemotactic behavior (e.g., attraction) in response to environmental stimuli. The results highlight the potential of utilizing DeepRL methods in conjunction with the neuromechanical model to infer potential neural interactions and circuitry that lead to specific behaviors.