Representing Where along with What Information in a Model of a Cortical Patch

Abstract

Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects. Forming a coherent picture of our surrounding environment requires combining visual information about the position of objects (where information) with information about their identity (what information). It also requires the ability to maintain this combined information for short periods of time after the stimulus is removed. Here, we propose a theoretical model of how this is accomplished in the brain, particularly when sensory input is incomplete, and missing what information should be supplied from what is stored in memory. The main idea is that local connectivity in cortical networks can allow the formation of localised states of activity. Where information can then be represented by the position of such “bumps”, and what information by the fine structure of the neuronal activity within them. We show that there is a difficulty with implementing this idea: noise and heterogeneity in connectivity cause bumps to drift, thereby losing where information. This problem can be solved by incorporating a localised increase in neuronal gain; this, however, interferes with retrieving what information and maintaining it in working memory. We quantify this interference via theoretical analysis of the model and show that, despite the interference, the proposed mechanism is an efficient one in retrieving what information while representing where information.