Organization and priming of long-term memory representations with two-phase plasticity

Synaptic tagging and capture (STC) is a molecular mechanism that accounts for the consolidation of synaptic changes induced by plasticity. To link this mechanism to long-term memory and thereby to the level of behavior, its dynamics on the level of recurrent networks have to be understood. To this end, we employ a biologically detailed neural network model of spiking neurons featuring STC, which models the learning and consolidation of long-term memory representations. Using this model, we investigate the effects of different organizational paradigms of multiple memory representations, and demonstrate a proof of principle for priming on long timescales. We examine these effects considering the spontaneous activation of memory representations as the network is driven by background noise. Our first finding is that the order in which the memory representations are learned significantly biases the likelihood of spontaneous activation towards more recently learned memory representations. Secondly, we find that hub-like structures counter this learning order effect for representations with less overlaps. We show that long-term depression is the mechanism underlying these findings, and that intermediate consolidation in between learning the individual representations strongly alters the described effects. Finally, we employ STC to demonstrate the priming of a long-term memory representation on a timescale of minutes to hours. As shown by these findings, our model provides a mechanistic synaptic and neuronal basis for known behavioral effects.