Noise Correlations Have Little Influence on the Coding of Selective Attention in Area V1

Abstract

Neurons in the visual primary cortex (area V1) do not only code simple features but also whether image elements are attended or not. These attentional signals are weaker than the feature-selective responses, and their reliability may therefore be limited by the noisiness of neuronal responses. Here we show that it is possible to decode the locus of attention on a single trial from the activity of a small population of neurons in area V1. Previous studies suggested that correlations between the activities of neurons that are part of a population limit the information gain, but here we report that the impact of these noise correlations depends on the relative position of the neurons' receptive fields. Correlations reduce the benefit of pooling neuronal responses evoked by the same object but actually enhance the advantage of pooling responses evoked by different objects. These opposing effects cancelled each other at the population level, so that the net effect of the noise correlations was negligible and attention could be decoded reliably. Our results suggest that noise correlations are caused by large-scale fluctuations in cortical excitability, which can be removed by a comparison of the response strengths evoked by different objects