Preference Conditioned State Persistence for Spiking Sequential Recommendation

Sequential recommendation relies on recurrent states that retain interaction history while remaining responsive to the current preference context. Adaptive spiking neurons can modulate state persistence, but existing formulations primarily characterize retention coefficients rather than how those coefficients are assigned to recurrent state groups. We introduce Preference-Conditioned Dynamics (PCD), which predicts groupwise retention from the current preference representation and recurrent history. The resulting recurrent transition is determined not only by the retention coefficients but also by their assignment to fixed state groups. To isolate this assignment, we replay learned membrane-retention sequences and permute coefficients across groups while preserving the coefficient multiset and mean at every event. On Amazon Video Games, reassignment produces measurable changes in PCD ranking metrics and substantially smaller responses in a Dynamic Gated Neuron comparator with the same recurrent-cell parameter count. MovieLens 1M reproduces this contrast for normalized discounted cumulative gain and mean reciprocal rank at cutoff 10. Controlled training variants and complementary CIFAR-10 experiments further show that the learned retention structure depends on conditioning-feature correspondence and on gradient access to that feature. These results show that marginal retention statistics are insufficient to characterize the functional state-persistence structure learned by recurrent Spiking Neural Networks.