Emergent multiscale dynamics in photonic neurons with dual feedback

Complex dynamical systems are inherently governed by multiscale dynamics, where processes on different temporal, spatial, and intensity scales interact through feedback mechanisms. The coupling between fast and slow dynamics often leads to nontrivial emergent behaviour, making a multiscale approach essential for understanding and modeling such systems. In particular, the mutual influence between fast and slow processes-where fast dynamics can modulate slow evolution, and slow dynamics can shape the conditions for fast processes-plays a key role, especially when coarse-graining across scales to capture the system's effective behaviour. Here, I study the dynamics of fast and slow events in the time series of a photonic neuron with dual feedback. Analysis of inter-peak intervals and ordinal analysis unveils rich multiscale interactions, where fast peaks and slow spikes cooperatively generate emergent behaviour. I also find how dual feedback enhances and stabilizes temporal correlations across multiple scales. These findings demonstrate how multiscale interaction can generate complex, emergent behaviour in controllable photonic systems, with implications for understanding other complex dynamical systems.