Two-stage synaptic plasticity enables memory consolidation during neuronal burst firing regimes

Abstract Neural circuits routinely alternate between input-driven tonic activity and collective burst firing. In the presence of Hebbian plasticity, bursts generate a robust attractor in weight space, creating a built-in drift that can be repurposed into a stabilizing trace of prior learning. We show that this phenomenon can be harnessed for memory consolidation through the introduction of a two-stage synaptic rule. The effective synaptic weight is defined as the product of a primary weight—updated by a Hebbian rule during both tonic and burst periods—and a secondary weight that updates in proportion with a coupling gain to the negative time-derivative of the primary weight. In a MNIST-like task, alternating tonic and burst epochs preserves earlier patterns, improves generalization to unseen inputs, and resists interference and noise, whereas replacing burst by quiescence or additional tonic epochs does not. Parameter sweeps reveal that coupling gain and the initial synaptic weights control whether bursts consolidate (“up-selection”) or prune (“down-selection”) synapses. Pairing the rule with alternative primary plasticity models yields distinct treatments of overlapping inputs, enabling either integration or separation. Studying switches in firing activity with a two-stage synaptic plasticity provides a plausible route to consolidation in biological and neuromorphic networks. Significance Statement Neural circuits alternate between tonic spiking and burst firing, yet most models of synaptic plasticity are limited to a single firing regime. We introduce a two—stage synaptic rule in which a primary weight encodes activity during both states, while a secondary weight—engaged only during bursts— stabilizes learning from tonic periods. In conductance-based networks and a pattern recognition task, this rule preserves memories, improves generalization, and resists interference, whereas quiescence or extended tonic activity do not. The model further shows that bursts can consolidate or prune synapses depending on coupling gain and initial conditions. These findings identify a plausible, biologically motivated mechanism for how activity state transitions shape memory consolidation. Competing Interest Statement The authors have declared no competing interest. Footnotes Declaration of interests The authors declare no competing interests. Declaration of generative AI and AI-assisted technologies ChatGPT was used to correct for spelling. This revised version is an expanded update of the previous preprint. Beyond adding deeper analyses and broader interpretations, it introduces a refined conceptual framework of the two-stage plasticity. We have also improved the overall language and presentation for clarity.