On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations, even if unknown to a downstream readout, do not impair the readout accuracy despite inducing limited-range correlations, whereas fluctuations of the attended feature can in principle limit behavioral performance.

Covert attention is one of the most widely studied examples of top-down modulation of neural activity in the visual system. Recent studies argue that attention improves behavioral performance by shaping of the noise distribution to suppress shared variability rather than by increasing response gain. Our work shows, however, that latent, trial-to-trial fluctuations of the focus and strength of attention lead to shared variability that is highly consistent with known experimental observations. Interestingly, fluctuations in the strength of attention do not affect coding performance. As a consequence, the experimentally observed changes in response variability may not be a mechanism of attention, but rather a side effect of attentional allocation strategies in different behavioral contexts.