Optimization of connectome weights for a neural network model generating both forward and backward locomotion in C. elegans

Previous studies tracking the relationship between manipulations of C. elegans neurons and the resulting behavioral changes have called for the development of a connectome-constrained neural network model that describes the cascade from neurons to behavior. However, the model using anatomical connectome weights directly did not achieve that. Here, we introduce the concept of learning the synaptic weights in our connectome-constrained neural network model based on the leaky-integrator equation while preserving the structural proportions of anatomical synapses. In this process, the weights of gap junctions and chemical synapses in C. elegans neurons are optimized. As a result, our neural network model generates plausible C. elegans behavior mediated by activity changes in forward and backward command-neurons, even without the introduction of pacemaker neurons with intrinsic oscillatory activity. Additionally, we identified necessary or sufficient neurons for maintaining oscillatory patterns on muscular activity that could serve as clues for the central pattern generator in our neural network model. Finally, we provide 10 optimized synaptic weight sets of C. elegans that reproduce the results of manipulation experiments on the SMD neurons. This study will facilitate future studies for unraveling the multiscale relationship of “from synapse to behavior” in the nervous system.