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Cortical networks can maintain memories for decades despite the shortlifetime of synaptic strength. Can a neural network store long-lasting memoriesin unstable synapses? Here, we study the effects of random noise on thestability of memory stored in synapses of an attractor neural network. Themodel includes ongoing spike timing dependent plasticity (STDP). We show thatcertain classes of STDP rules can lead to the stabilization of memory patternsstored in the network. The stabilization results from rehearsals induced bynoise. We show that unstructured neural noise, after passing through therecurrent network weights, carries the imprint of all memory patterns intemporal correlations. Under certain conditions, STDP combined with thesecorrelations, can lead to reinforcement of all existing patterns, even thosethat are never explicitly visited. Thus, unstructured neural noise canstabilize the existing structure of synaptic connectivity. Our findings mayprovide the functional reason for highly irregular spiking displayed bycortical neurons and provide justification for models of system memoryconsolidation. Therefore, we propose that irregular neural activity is thefeature that helps cortical networks maintain stable connections.

Long-term memory stabilized by noise-induced rehearsal