Stimulus-dependent variability and noise correlations in cortical MT neurons

Abstract

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.C ortical 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 secondorder 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 trialby-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 differ...

Stochastic transitions into silence cause noise correlations in cortical circuits

“…First, except in ref. 13, the average correlation in these networks was negligible. This is because either neuronal variability across the network was independent (16) or because the transitions between network discrete states involved the coordination of small subpopulations of cells (17,18).…”

“…We next studied the dynamics of the population in response to short acoustic clicks (duration, 5 ms; interclick interval, 2.5 or 3.5 s). We used a sliding spike count window (T = 50 ms) and computed the averaged instantaneous rate, spike count correlation ρ(t) (2,14,15), and spike count Fano factor (11)(12)(13) by performing the statistics across repeated stimulus presentations and averaged over single units or single-unit pairs (Methods). Similarly, we computed the instantaneous silence density S(t) using 20-ms bins.…”

“…The proposed mechanisms range from suppression of an ongoing chaotic state (16), to the quenching of variability produced by the transitions of the network among multiple discrete states (17,18) or along a line attractor (13). Our model shares the idea that the stimulus puts the system away from the bifurcation where network state transitions occur.…”

“…Several studies have recently modeled (13,(16)(17)(18)) the dynamics of stimulus-evoked neuronal variability (11). The proposed mechanisms range from suppression of an ongoing chaotic state (16), to the quenching of variability produced by the transitions of the network among multiple discrete states (17,18) or along a line attractor (13).…”

“…In anesthetized rodents correlations decrease with brain state desynchronization (8,9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11)(12)(13) seems to occur jointly with a transient decrease in correlation (2,14,15). These observations suggest that correlations reflect the dynamical state of the circuit more than its hardwired connectivity.…”