Revisiting the Haken Lighthouse model

Abstract Simple spiking neural network models, such as those built from interacting integrate-and-fire (IF) units, exhibit rich emergent behaviours but remain notoriously difficult to analyse, particularly in terms of their pattern-forming properties. In contrast, rate-based models and coupled phase oscillators offer greater mathematical tractability but fail to capture the full dynamical complexity of spiking networks. To bridge these modelling paradigms, Hermann Haken—the pioneer of Synergetics—introduced the Lighthouse model, a framework that provides insights into synchronisation, travelling waves, and pattern formation in neural systems. In this work, we revisit the Lighthouse model and develop new mathematical results that deepen our understanding of self-organisation in spiking neural networks. Specifically, we derive the linear stability conditions for phase-locked spiking states in Lighthouse networks structured on graphs with realistic synaptic interactions $$(\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>α</mml:mi> </mml:mrow> </mml:math> -function synapses) and axonal conduction delays. Extending the analysis on graphs to a spatially continuous (non-local) setting, we develop a variant of Turing instability analysis to explore emergent spiking patterns. Finally, we show how localised spiking bump solutions—which are difficult to mathematically analyse in IF networks—are far more tractable in the Lighthouse model and analyse their linear stability to wandering states. These results reaffirm the Lighthouse model as a valuable tool for studying structured neural interactions and self-organisation, further advancing the synergetic perspective on spiking neural dynamics.