Sparseness constrains the prolongation of memory lifetime via synaptic metaplasticity.

Synaptic changes impair previously acquired memory traces. The smaller this impairment the larger is the longevity of memories. Two strategies have been suggested to keep memories from being overwritten too rapidly while preserving receptiveness to new contents: either introducing synaptic meta levels that store the history of synaptic state changes or reducing the number of synchronously active neurons, which decreases interference. We find that synaptic metaplasticity indeed can prolong memory lifetimes but only under the restriction that the neuronal population code is not too sparse. For sparse codes, metaplasticity may actually hinder memory longevity. This is important because in memory-related brain regions as the hippocampus population codes are sparse. Comparing 2 different synaptic cascade models with binary weights, we find that a serial topology of synaptic state transitions gives rise to larger memory capacities than a model with cross transitions. For the serial model, memory capacity is virtually independent of network size and connectivity.