Stimulus-dependent variability and noise correlations in cortical MT neurons

Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.Cortical activity is highly variable during spontaneous activity (1–4) and even when tested under constant experimental conditions (5–8). This variability is thought to limit the capacity of individual neurons to transmit information (9). Furthermore, variability is often correlated among neurons, and thus, it cannot be completely removed by averaging the population response (9–12). Recent experimental studies have examined the second-order statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13–17). In particular, it has been shown that the Fano factor (FF)—that is, the ratio between the variance of the spike counts over trials and its mean—is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus. Importantly, both preferred and nonpreferred stimuli reduced the FF. In addition, the evoked noise correlation—that is, the trial-to-trial covariance of stimulus induced activity between two simultaneously recorded neurons—is also reduced upon stimulus presentation (18), after stimulus adaptation (19) or perceptual learning (20), and under attention (14, 21), an effect that could, under certain conditions, lead to more reliable estimates of the mean population activity (22). Hence, there is a growing body of evidence suggesting that the encoding of a signal through cortical activity may be improved by minimizing both trial-by-trial variability and noise correlations. However, it remains an open experimental and theoretical question, whether these statistics are themselves tuned to different stimulus features, an aspect that may be overlooked when only analyzing preferred and nonpreferred stimuli.Here, we examined the statistics of responses of area–middle temporal (MT) neurons in awake, fixating primates, to moving gratings and different plaid patterns of different directions, as well as moving gratings of different luminance contrasts. Specifically, we examined the baseline levels and the evoked directional and contrast tuning of the FF of individual neurons and the noise correlations between pairs of neurons with similar direction preferences. To get further theoretical insight, we investigated the effect of an applied stimulus on variability and correlations in an extended ring network model implementing direction selectivity (23).We found that both the trial-by-trial variability and the noise correlations among MT neurons showed a directional tuning that is not trivially explained by firing rate variations alone. We demonstrated that the tuning of these second-order statistics is well explained by a ring model operating near or beyond a bifurcation separating a single homogeneous state regime and a regime of multistability. Finally, we evaluated the impact of tuned second-order statistics on the accuracy of the population code.