Neuronal Mechanisms of Selective Attention in Early Vision

ABSTRACT: Neuronal mechanisms of selective attention in early vision The mechanisms of selective attention have been intensively investigated over the past two decades. As a consequence, there is now a better understanding of the network of brain areas that are involved in visual attention and the responses of individual neurons to attentional shifts. Current models of attention are also one step closer to providing a general framework of attentional circuits that could have direct clinical significance. However, a major limitation of current models is the lack of cellular specificity. Most current models treat each cell within a given cortical area as if it was taken from a random sample of neurons that simply differ in the spatial location of their receptive fields and their selectivity for line orientation and direction of movement. The existence of different cell types with possible different functions is completely neglected. Neurons in different layers, neurons making local and long-range connections and, until very recently, inhibitory and excitatory neurons are all treated equally by models. The main goal of this proposal is to provide the biological data on cell selectivity that is needed to build more realistic and clinically relevant models of visual attention. We aim to characterize the different cell types that are involved in attentional networks in area V1. We also aim to identify populations of neurons with different functions in attention: those involved in enhancing vision at the focus of attention and those involved in suppressing distraction in surrounding areas. We will use state-of-the-art technology to densely sample individual neurons and populations of neurons through the cortical depths of area V1. Our novel technology allows us to study the properties of a given population of neurons for days or months, if necessary. We aim to take advantage of this technology to provide a detailed characterization of attention circuits at different cortical depths of area V1 and identify the task parameters that make attentional modulations strongest.