Capacity limits in oscillatory networks: Implications for sensory coding

Psychological studies have investigated limits in the capacity to simultaneously store multiple objects in working memory, which turns out to be approximately four. In this paper we examine the existence and origin of such a capacity limit in the sensory encoding stage, where synchronous activity can be considered to group related features of a common object. We develop a model of an object recognition network using oscillatory elements that can achieve phase synchronization. Using simulations based on this network, we show that distinct phases of oscillation can be used to label combinations of objects presented simultaneously. This allows the network to separate mixtures of objects, and identify the input elements that belong to each object. We demonstrate that there is a limit of four objects that can be separated from a mixture. Further studies are required to generalize this result by varying the size of the network and the number of objects used for training. We also show that by narrowing a tuning function that governs the dynamics of the system, we can achieve higher separation accuracy. However, this comes at the cost of utilizing a higher number of iterations over which the system settles and learns its synaptic weights. We lay down a framework for the quantitative modeling of factors affecting capacity limits, which has the potential to advance our understanding of sensory representation, attention, working memory and multitasking.