Loss of Plasticity and Its Recovery via Replay-Driven Compression in a Spiking Model of Hippocampal CA3

Artificial neural networks trained sequentially on new information are prone to interference: new learning can degrade or overwrite what was previously acquired. Research into this failure mode has recently led to a distinction between catastrophic forgetting, akin to overwriting, and loss of plasticity, in which both older and newer memories are degraded. Here we ask whether similar limitations arise in biologically-grounded spiking neural networks and study how nature might address them. In a biologically detailed recurrent circuit that models the mammalian hippocampus, multiple memory traces (modeling spatial environments) are learned sequentially by a common network and evaluated through their spontaneous and cue-evoked replay. We find that sequential learning progressively saturates recurrent connectivity, increasing interference among memories and eventually eliminating the network's ability to replay stored representations. This collapse resembles loss of plasticity more closely than classical catastrophic forgetting. We then show that alternating online learning with offline and spontaneous replay-driven synaptic compression counteracts this saturation: it reorganizes the recurrent weight distribution while preserving recall, allowing the network to store additional memory traces. Together, these results identify synaptic compression and plasticity schemes organized around spontaneous replay as complementary and neurobiologically-grounded strategies for mitigating interference in recurrent spiking memory networks. This identification connects memory capacity limits observed in biology to an active distinction in the machine learning literature between loss of plasticity and catastrophic forgetting.