Off-line memory reprocessing in a recurrent neuronal network formed by unsupervised learning

Abstract

AbstractIn the visual cortex, memory traces for complex objects are embedded into a scaffold of feed-forward and recurrent connectivity of the hierarchically organized visual pathway. Strong evidence suggests that consolidation of the memory traces in such a memory network depends on an off-line reprocessing done in the sleep state or during restful waking. It remains largely unclear, what plasticity mechanisms are involved in this consolidation process and what changes are induced in the network during memory reprocessing in the off-line regime. Here we focus on the functional consequences off-line reprocessing has in a hierarchical recurrent neuronal network that learns different person identities from natural face images in an unsupervised manner. Due to the inherently self-exciting, but competitive unit dynamics, the two-layered network is able to self-generate sparse activity even in the absence of external input in an off-line regime. In this regime, the network replays the memory content established during preceding on-line learning. Remarkably, this off-line memory replay turns out to be highly beneficial for the network recognition performance. The benefit is articulated after the off-line regime in a strong boost of identity recognition rate on the alternative face views to which the network has not been exposed during learning. Performance of both network layers is affected by the boost. Surprisingly, the positive effect is independent of synapse-specific plasticity, relying completely on a synapse-unspecific mechanism of homeostatic activity regulation that tunes network unit excitability. Comparing further a purely feed-forward configuration of the network with its fully recurrent original version reveals a stronger boost in recognition performance for the latter after the off-line reprocessing. These findings suggest that the off-line memory reprocessing enhances generalization capability of the hierarchical recurrent network by improving communication of contextual cues mediated via recurrent lateral and top-down connectivity.