Emergent bifurcation and robust decision-making in neurodynamic swarms

Collective motion is typically modeled by local velocity-alignment rules. However, these models do not fully capture recent evidence for complex decision-making in biological swarms and are fragile to structured, adversarial perturbations. Here, we introduce a neurodynamic swarm model where collective behavior emerges not from alignment, but from nonlinear competition within Hopfield-type ring attractors. We show that this mechanism naturally gives rise to emergent decision bifurcations that reproduce sophisticated animal behaviors. Crucially, the inherent stability of the attractors provides the swarm with remarkable resilience, allowing it to maintain coherence against adversarial perturbations that shatter alignment-based models.