Correlated structure of neuronal firing in macaque visual cortex limits information for binocular depth discrimination.

Variability in cortical neural activity potentially limits sensory discriminations. Theoretical work shows that information required to discriminate two similar stimuli is limited by the correlation structure of cortical variability. We investigated these information-limiting correlations by recording simultaneously from visual cortical areas primary visual cortex (V1) and extrastriate area V4 in macaque monkeys performing a binocular, stereo depth discrimination task. Within both areas, noise correlations on a rapid temporal scale (20-30 ms) were stronger for neuron pairs with similar selectivity for binocular depth, meaning that these correlations potentially limit information for making the discrimination. Between-area correlations (V1 to V4) were different, being weaker for neuron pairs with similar tuning and having a slower temporal scale (100+ ms). Fluctuations in these information-limiting correlations just prior to the detection event were associated with changes in behavioral accuracy. Although these correlations limit the recovery of information about sensory targets, their impact may be curtailed by integrative processing of signals across multiple brain areas.NEW & NOTEWORTHY Correlated noise reduces the stimulus information in visual cortical neurons during experimental performance of binocular depth discriminations. The temporal scale of these correlations is important. Rapid (20-30 ms) correlations reduce information within and between areas V1 and V4, whereas slow (>100 ms) correlations between areas do not. Separate cortical areas appear to act together to maintain signal fidelity. Rapid correlations reduce the neuronal signal difference between stimuli and adversely affect perceptual discrimination.

The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance.

Spike-time correlations capture the short timescale covariance between the activity of neurons on a single trial. These correlations can significantly vary in magnitude and sign from trial to trial, and have been proposed to contribute to information encoding in visual cortex. While monkeys performed a motion-pulse detection task, we examined the behavioral impact of both the magnitude and sign of single-trial spike-time correlations between two nonoverlapping pools of middle temporal (MT) neurons. We applied three single-trial measures of spike-time correlation between our multiunit MT spike trains (Pearson's, absolute value of Pearson's, and mutual information), and examined the degree to which they predicted a subject's performance on a trial-by-trial basis. We found that on each trial, positive and negative spike-time correlations were almost equally likely, and, once the correlational sign was accounted for, all three measures were similarly predictive of behavior. Importantly, just before the behaviorally relevant motion pulse occurred, single-trial spike-time correlations were as predictive of the performance of the animal as single-trial firing rates. While firing rates were positively associated with behavioral outcomes, the presence of either strong positive or negative correlations had a detrimental effect on behavior. These correlations occurred on short timescales, and the strongest positive and negative correlations modulated behavioral performance by ∼9%, compared with trials with no correlations. We suggest a model where spike-time correlations are associated with a common noise source for the two MT pools, which in turn decreases the signal-to-noise ratio of the integrated signals that drive motion detection.SIGNIFICANCE STATEMENT Previous work has shown that spike-time correlations occurring on short timescales can affect the encoding of visual inputs. Although spike-time correlations significantly vary in both magnitude and sign across trials, their impact on trial-by-trial behavior is not fully understood. Using neural recordings from area MT (middle temporal) in monkeys performing a motion-detection task using a brief stimulus, we found that both positive and negative spike-time correlations predicted behavioral responses as well as firing rate on a trial-by-trial basis. We propose that strong positive and negative spike-time correlations decreased behavioral performance by reducing the signal-to-noise ratio of integrated MT neural signals.

Neural architectures for stereo vision.

Stereoscopic vision delivers a sense of depth based on binocular information but additionally acts as a mechanism for achieving correspondence between patterns arriving at the left and right eyes. We analyse quantitatively the cortical architecture for stereoscopic vision in two areas of macaque visual cortex. For primary visual cortex V1, the result is consistent with a module that is isotropic in cortical space with a diameter of at least 3 mm in surface extent. This implies that the module for stereo is larger than the repeat distance between ocular dominance columns in V1. By contrast, in the extrastriate cortical area V5/MT, which has a specialized architecture for stereo depth, the module for representation of stereo is about 1 mm in surface extent, so the representation of stereo in V5/MT is more compressed than V1 in terms of neural wiring of the neocortex. The surface extent estimated for stereo in V5/MT is consistent with measurements of its specialized domains for binocular disparity. Within V1, we suggest that long-range horizontal, anatomical connections form functional modules that serve both binocular and monocular pattern recognition: this common function may explain the distortion and disruption of monocular pattern vision observed in amblyopia.This article is part of the themed issue 'Vision in our three-dimensional world'.

Dynamics of the functional link between area MT LFPs and motion detection.

The evolution of a visually guided perceptual decision results from multiple neural processes, and recent work suggests that signals with different neural origins are reflected in separate frequency bands of the cortical local field potential (LFP). Spike activity and LFPs in the middle temporal area (MT) have a functional link with the perception of motion stimuli (referred to as neural-behavioral correlation). To cast light on the different neural origins that underlie this functional link, we compared the temporal dynamics of the neural-behavioral correlations of MT spikes and LFPs. Wide-band activity was simultaneously recorded from two locations of MT from monkeys performing a threshold, two-stimuli, motion pulse detection task. Shortly after the motion pulse occurred, we found that high-gamma (100-200 Hz) LFPs had a fast, positive correlation with detection performance that was similar to that of the spike response. Beta (10-30 Hz) LFPs were negatively correlated with detection performance, but their dynamics were much slower, peaked late, and did not depend on stimulus configuration or reaction time. A late change in the correlation of all LFPs across the two recording electrodes suggests that a common input arrived at both MT locations prior to the behavioral response. Our results support a framework in which early high-gamma LFPs likely reflected fast, bottom-up, sensory processing that was causally linked to perception of the motion pulse. In comparison, late-arriving beta and high-gamma LFPs likely reflected slower, top-down, sources of neural-behavioral correlation that originated after the perception of the motion pulse.

ROC-based estimates of neural-behavioral covariations using matched filters.

Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus.

The functional link between area MT neural fluctuations and detection of a brief motion stimulus.

Fluctuations of neural firing rates in visual cortex are known to be correlated with variations in perceptual performance. It is important to know whether these fluctuations are functionally linked to perception in a causal manner or instead reflect non-causal processes that arise after the perceptual decision is made. We recorded from middle temporal (MT) neurons from monkey subjects while they detected the random occurrence of a brief 50 ms motion pulse that occurred in either of two (or simultaneously in both) random dot patches located in the same hemisphere. The receptive field parameters of the motion pulse were matched to that preferred by each MT neuron under study. This task contained uncertainty in both space and time because, on any given trial, the subjects did not know which patch would contain the motion pulse or when the motion pulse would occur. Covariations between MT activity and behavior began just before the motion pulse onset and peaked at the maximum neural response. These neural-behavioral covariations were strongest when only one patch contained the motion pulse and were still weakly present when a patch did not contain a motion pulse. A feedforward temporal integration model with two independent detector channels captured both the detection performance and evolution of the neural-behavior covariations over time and stimulus condition. The results suggest that, when detecting a brief visual stimulus, there is a causal relationship between fluctuations in neural activity and variations in behavior across trials.

The effect of microsaccades on the correlation between neural activity and behavior in middle temporal, ventral intraparietal, and lateral intraparietal areas.

It is widely reported that the activity of single neurons in visual cortex is correlated with the perceptual decision of the subject. The strength of this correlation has implications for the neuronal populations generating the percepts. Here we asked whether microsaccades, which are small, involuntary eye movements, contribute to the correlation between neural activity and behavior. We analyzed data from three different visual detection experiments, with neural recordings from the middle temporal (MT), lateral intraparietal (LIP), and ventral intraparietal (VIP) areas. All three experiments used random dot motion stimuli, with the animals required to detect a transient or sustained change in the speed or strength of motion. We found that microsaccades suppressed neural activity and inhibited detection of the motion stimulus, contributing to the correlation between neural activity and detection behavior. Microsaccades accounted for as much as 19% of the correlation for area MT, 21% for area LIP, and 17% for VIP. While microsaccades only explain part of the correlation between neural activity and behavior, their effect has implications when considering the neuronal populations underlying perceptual decisions.

Population toxicokinetics of benzene.

In assessing the distribution and metabolism of toxic compounds in the body, measurements are not always feasible for ethical or technical reasons. Computer modeling offers a reasonable alternative, but the variability and complexity of biological systems pose unique challenges in model building and adjustment. Recent tools from population pharmacokinetics, Bayesian statistical inference, and physiological modeling can be brought together to solve these problems. As an example, we modeled the distribution and metabolism of benzene in humans. We derive statistical distributions for the parameters of a physiological model of benzene, on the basis of existing data. The model adequately fits both prior physiological information and experimental data. An estimate of the relationship between benzene exposure (up to 10 ppm) and fraction metabolized in the bone marrow is obtained and is shown to be linear for the subjects studied. Our median population estimate for the fraction of benzene metabolized, independent of exposure levels, is 52% (90% confidence interval, 47-67%). At levels approaching occupational inhalation exposure (continuous 1 ppm exposure), the estimated quantity metabolized in the bone marrow ranges from 2 to 40 mg/day.