Scaling Properties for Artificial Neural Network Models of a Small Nervous System
We trained a range of neural-network architectures to predict whole-brain activity in C. elegans and measured how prediction quality scales with the amount of data, the number of parameters, and the choice of architecture. Prediction error falls logarithmically as training data grows, consistently across datasets, while model size has a nonlinear effect.