Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve

Abstract: The human lifespan retrieval curve describes the proportion of recalled memories from each year of life. It exhibits a reminiscence bump - a tendency for aged people to better recall memories formed during their young adulthood than from other periods of life. We have modelled this using an attractor Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. We systematically studied the synaptic mechanisms underlying the reminiscence bump in this network model after introduction of an exponential decay of the synaptic learning rate and examined its sensitivity to network size and other relevant modelling mechanisms. The most influential parameters turned out to be the synaptic learning rate at birth and the time constant of its exponential decay with age, which set the bump position in the lifespan retrieval curve. The other parameters mainly influenced the general magnitude of this curve. Furthermore, we introduced the parametrization of the recency phenomenon - the tendency to better remember the most recent memories - reflected in the curve's upwards tail in the later years of the lifespan. Such recency was achieved by adding a constant baseline component to the exponentially decaying synaptic learning rate.