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

Abstract

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.

Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations

“…Consistent with studies evaluating the readout 115 hypothesis, attention increases correlated variability between the two areas 9,10,25 (Supplementary 116 Figure 1C). This increase between areas suggests that attention-related effects are not simply due 117 to global reductions in slow fluctuations, which has recently been hypothesized to explain 118 attention-related correlation decreases within a single brain area 26 ( Supplementary Figure 2 and 119 Supplementary Text). Further, this attention-related increase was weakly dependent on the visual 120 responsivity of SC neurons ( Supplementary Figure 3).…”

Cortical Variability and Challenges for Modeling Approaches

“…Importantly, compelling evidence suggests that a substantial portion of the spontaneous and evoked total and shared variability is attributable to global fluctuations (Ecker et al, 2014, 2016; Goris et al, 2014; Mochol et al, 2015; Pachitariu et al, 2015; Schölvinck et al, 2015; Arandia-Romero et al, 2016); and this has direct implications in neural coding in visual (Lin et al, 2015; Arandia-Romero et al, 2016; Ecker et al, 2016) and in auditory areas (Mochol et al, 2015; Pachitariu et al, 2015). For instance, high population activity in monkey V1 increases the information that a subset of neuronal ensembles carry about stimulus orientation, only the ones that show a strong multiplicative modulation.…”

“…These recent modeling efforts aim to pin down when precisely during the course of the trial (Moreno-Bote et al, 2014; Bujan et al, 2015; Doiron et al, 2016) and in which specific network state (Arandia-Romero et al, 2016) noise correlations are informative or deleterious for neural coding (Ecker et al, 2014, 2016; Moreno-Bote et al, 2014; Lin et al, 2015; Pachitariu et al, 2015; Schölvinck et al, 2015; Arandia-Romero et al, 2016; Doiron et al, 2016). …”

From the statistics of connectivity to the statistics of spike times in neuronal networks

“…Patterns of correlations in cortical micro-circuits have been related to connection probabilities and strengths [14]. At the same time, latent variable models of dynamics applied to cortical data have revealed a strong impact of global inputs to the population [15,16••,17,18]. …”