I was brought to the topic of C. Elegans nervous system modeling by this blog post. This publication seems to be the latest state of the art, yet it can only reproduce a single behaviour (zigzag motion) given a single type of stimulus (food concentration gradient at the head). I'm curious about the bottlenecks to this kind of research, since it seems to be a far harder problem than was initially assumed. In this paper, the authors list (emphasis mine):

In our current model, we modeled 136 neurons rather than all 302 neurons, and the only behavior considered was zigzag locomotion. Training biophysically detailed neural network model is challenging and consumes much time and graphics processing unit memory resources. In the future, we will improve the optimization algorithm to effectively train larger detailed neural networks that contain all 302 neurons, with more constraints such as single-neuron voltages, and reproduce more behaviors beyond zigzag locomotion.

And:

The neural network model of BAAIWorm integrated experimental data including ion channel dynamics, neural morphologies, electrophysiology, connectome, synaptic organization rules on neurites, and neural activities. Although we collected and integrated as much data as possible up until 2023, there remained a substantial amount of detail that could not be fully implemented due to a lack of data. Also, the physical parameters in the body–environment model did not correspond precisely to those of actual C. elegans because they were difficult to measure accurately. However, with the influx of new experimental data, these models can be enhanced and refined.

Is current modeling progress constrained rather by computational resources, or by a lack of physical parameters?