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The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations.

Genetically encoded voltage indicators (GEVIs) promise to reveal the membrane potential of genetically targeted neuronal populations through noninvasive, chronic imaging of large portions of cortical space. Here we test a promising GEVI in mouse cortex during wakefulness, a challenging condition due to large hemodynamic activity, and we introduce a straightforward projection method to separate a signal dominated by membrane voltage from a signal dominated by hemodynamic activity. We expressed VSFP-Butterfly 1.2 plasmid in layer 2/3 pyramidal cells of visual cortex through electroporation in utero. We then used wide-field imaging with two cameras to measure both fluorophores of the indicator in response to visual stimuli. By taking weighted sums and differences of the two measurements, we obtained clear separation of hemodynamic and voltage signals. The hemodynamic signal showed strong heartbeat oscillations, superimposed on slow dynamics similar to blood oxygen level-dependent (BOLD) or “intrinsic” signals. The voltage signal had fast dynamics similar to neural responses measured electrically, and showed an orderly retinotopic mapping. We compared this voltage signal with calcium signals imaged in transgenic mice that express a calcium indicator (GCaMP3) throughout cortex. The voltage signal from VSFP had similar signal-to-noise ratios as the calcium signal, it was more immune to vascular artifacts, and it integrated over larger regions of visual space, which was consistent with its reporting mostly subthreshold activity rather than the spiking activity revealed by calcium signals. These results demonstrate that GEVIs provide a powerful tool to study the dynamics of neural populations at mesoscopic spatial scales in the awake cortex.

Numerous studies have revealed traveling waves of activity in sensory cortex, both following sensory stimulation and during ongoing activity. We contributed to this body of work by measuring the spike-triggered average of the local field potential (stLFP) at multiple concurrent locations (Nauhaus et al., 2009) in the visual cortex of anesthetized cats and macaques. We found the stLFP to be progressively delayed at increasing distances from the site of the triggering spikes, and interpreted this as a traveling wave of depolarization originating from that site. Our results were criticized, however, on two grounds. First, a study using the same recording techniques in the visual cortex of awake macaques reported an apparent lack of traveling waves, and proposed that traveling waves could arise artifactually from excessive filtering of the field potentials (Ray and Maunsell, 2011). Second, the interpretability of the stLFP was questioned (Kenneth Miller, personal communication), as the stLFP must reflect not only interactions between spike trains and field potentials, but also correlations within and across the spike trains. Here, we show that our data and interpretation are not imperiled by these criticisms. We reanalyzed our field potentials to remove any possible artifact due to filtering and to discount the effects of correlations within and across the triggering spike trains. In both cases, we found that the traveling waves were still present. In fact, closer inspection of Ray and Maunsell's (2011) data from awake cortex shows that they do agree with ours, as they contain clear evidence for traveling waves.

In the early visual system, a contrast gain control mechanism sets the gain of responses based on the locally prevalent contrast. The measure of contrast used by this adaptation mechanism is commonly assumed to be the standard deviation of light intensities relative to the mean (root-mean-square contrast). A number of alternatives, however, are possible. For example, the measure of contrast might depend on the absolute deviations relative to the mean, or on the prevalence of the darkest or lightest intensities. We investigated the statistical computation underlying this measure of contrast in the cat's lateral geniculate nucleus, which relays signals from retina to cortex. Borrowing a method from psychophysics, we recorded responses to white noise stimuli whose distribution of intensities was precisely varied. We varied the standard deviation, skewness, and kurtosis of the distribution of intensities while keeping the mean luminance constant. We found that gain strongly depends on the standard deviation of the distribution. At constant standard deviation, moreover, gain is invariant to changes in skewness or kurtosis. These findings held for both ON and OFF cells, indicating that the measure of contrast is independent of the range of stimulus intensities signaled by the cells. These results confirm the long-held assumption that contrast gain control computes root-mean-square contrast. They also show that contrast gain control senses the full distribution of intensities and leaves unvaried the relative responses of the different cell types. The advantages to visual processing of this remarkably specific computation are not entirely known.

The responses of neurons in lateral geniculate nucleus (LGN) exhibit powerful suppressive phenomena such as contrast saturation, size tuning, and masking. These phenomena cannot be explained by the classical center-surround receptive field and have been ascribed to a variety of mechanisms, including feedback from cortex. We asked whether these phenomena might all be explained by a single mechanism, contrast gain control, which is inherited from retina and possibly strengthened in thalamus. We formalized an intuitive model of retinal contrast gain control that explicitly predicts gain as a function of local contrast. In the model, the output of the receptive field is divided by the output of a suppressive field, which computes the local root-mean-square contrast. The model provides good fits to LGN responses to a variety of stimuli; with a single set of parameters, it captures saturation, size tuning, and masking. It also correctly predicts that responses to small stimuli grow proportionally with contrast: were it not for the suppressive field, LGN responses would be linear. We characterized the suppressive field and found that it is similar in size to the surround of the classical receptive field (which is eight times larger than commonly estimated), it is not selective for stimulus orientation, and it responds to a wide range of frequencies, including very low spatial frequencies and high temporal frequencies. The latter property is hardly consistent with feedback from cortex. These measurements thoroughly describe the visual properties of contrast gain control in LGN and provide a parsimonious explanation for disparate suppressive phenomena.

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single “standard model” for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

Cortical visual neurons in the cat and monkey are inhibited by stimuli surrounding their receptive fields (surround suppression) or presented within their receptive fields (cross-orientation or overlay suppression). We show that human contrast sensitivity is similarly affected by two distinct suppression mechanisms. In agreement with the animal studies, human surround suppression is tightly tuned to the orientation and spatial frequency of the test, unlike overlay suppression. Using a double-masking paradigm, we also show that in humans, overlay suppression precedes surround suppression in the processing sequence. Surprisingly, we find that, unlike overlay suppression, surround suppression is only strong in the periphery (>1° eccentricity). This result argues for a new functional distinction between foveal and peripheral operations.

Simple cells in the primary visual cortex often appear to compute a weighted sum of the light intensity distribution of the visual stimuli that fall on their receptive fields. A linear model of these cells has the advantage of simplicity and captures a number of basic aspects of cell function. It, however, fails to account for important response nonlinearities, such as the decrease in response gain and latency observed at high contrasts and the effects of masking by stimuli that fail to elicit responses when presented alone. To account for these nonlinearities we have proposed a normalization model, which extends the linear model to include mutual shunting inhibition among a large number of cortical cells. Shunting inhibition is divisive, and its effect in the model is to normalize the linear responses by a measure of stimulus energy. To test this model we performed extracellular recordings of simple cells in the primary visual cortex of anesthetized macaques. We presented large stimulus sets consisting of (1) drifting gratings of various orientations and spatiotemporal frequencies; (2) plaids composed of two drifting gratings; and (3) gratings masked by full-screen spatiotemporal white noise. We derived expressions for the model predictions and fitted them to the physiological data. Our results support the normalization model, which accounts for both the linear and the nonlinear properties of the cells. An alternative model, in which the linear responses are subject to a compressive nonlinearity, did not perform nearly as well.