Neurons as Worlds: Learning Adaptive Mesoscopic Dynamics from Particles

Coarse particle group representations can reduce the communication cost of particle simulation, but changing particle groups disconnects their current contents from the accumulated predictive context. The challenge is to reorganize the representation without restarting its history. We introduce Neuronal World Dynamics (NWD), which treats regrouping as a state transition of recurrent particle group representations called neuronal worlds. As the particle groups splits and merges, physical moments are recomputed from the unchanged particles, while compact memories are copied or combined locally. Shared recurrent dynamics then refine the inherited context using each successor's current observations, without replaying trajectories. The theoretical analysis identifies when a larger decoder cannot resolve coarse-state ambiguity and quantifies the trade-off between suppressing reorganization and retaining learned dynamics. Across four particle benchmarks, NWD achieves the lowest mean 100-step position NRMSE in all five settings, with 51.8–52.6% reductions over the recurrent-token baseline on N-body. On N-body, NWD reduces this error by 10.7–14.5% relative to fixed-cardinality variants and 51.8–52.6% relative to a recurrent-token baseline; memory ablations provide additional evidence for retained context and its association with particle support.