Yuzhi Chen

Sensory stimulation shifts visual cortex from synchronous to asynchronous states.

In the mammalian cerebral cortex, neural responses are highly variable during spontaneous activity and sensory stimulation. To explain this variability, the cortex of alert animals has been proposed to be in an asynchronous high-conductance state in which irregular spiking arises from the convergence of large numbers of uncorrelated excitatory and inhibitory inputs onto individual neurons. Signatures of this state are that a neuron’s membrane potential (Vm) hovers just below spike threshold, and its aggregate synaptic input is nearly Gaussian, arising from many uncorrelated inputs. Alternatively, irregular spiking could arise from infrequent correlated input events that elicit large fluctuations in Vm (refs 5, 6). To distinguish between these hypotheses, we developed a technique to perform whole-cell Vm measurements from the cortex of behaving monkeys, focusing on primary visual cortex (V1) of monkeys performing a visual fixation task. Here we show that, contrary to the predictions of an asynchronous state, mean Vm during fixation was far from threshold (14 mV) and spiking was triggered by occasional large spontaneous fluctuations. Distributions of Vm values were skewed beyond that expected for a range of Gaussian input, but were consistent with synaptic input arising from infrequent correlated events. Furthermore, spontaneous fluctuations in Vm were correlated with the surrounding network activity, as reflected in simultaneously recorded nearby local field potential. Visual stimulation, however, led to responses more consistent with an asynchronous state: mean Vm approached threshold, fluctuations became more Gaussian, and correlations between single neurons and the surrounding network were disrupted. These observations show that sensory drive can shift a common cortical circuitry from a synchronous to an asynchronous state.

Complex dynamics of V1 population responses explained by a simple gain-control model.

To understand sensory encoding and decoding, it is essential to characterize the dynamics of population responses in sensory cortical areas. Using voltage-sensitive dye imaging in awake, fixating monkeys, we obtained complete quantitative measurements of the spatiotemporal dynamics of V1 responses over the entire region activated by small, briefly presented stimuli. The responses exhibit several complex properties: they begin to rise approximately simultaneously over the entire active region, but reach their peak more rapidly at the center. However, at stimulus offset the responses fall simultaneously and at the same rate at all locations. Although response onset depends on stimulus contrast, both the peak spatial profile and the offset dynamics are independent of contrast. We show that these results are consistent with a simple population gain-control model that generalizes earlier single-neuron contrast gain-control models. This model provides valuable insight and is likely to be applicable to other brain areas.

Optimal Temporal Decoding of Neural Population Responses in a Reaction-Time Visual Detection Task

Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.

Uniform spatial spread of population activity in primate parafoveal V1

What are the shape and size of the region in primate V1 that processes information from a single point in visual space? This region, a fundamental property termed cortical point image (CPI) (McIlwain 1986), represents the minimal population of V1 neurons that can be activated by a visual stimulus and therefore has important implications for population coding in the cortex. Previous indirect attempts to measure the CPI in macaque V1 using sparse microelectrode recordings resulted in conflicting findings. Whereas some early studies suggested that CPI size is constant throughout V1 (e.g., Hubel and Wiesel 1974), others have reported large changes in CPI size in parafoveal V1 (e.g., Van Essen et al. 1984). To resolve this controversy, we used voltage-sensitive dye imaging in V1 of fixating monkeys to directly measure the subthreshold CPI and several related properties across a range of parafoveal eccentricities. We found that despite large changes in other properties of the retinotopic map, the subthreshold CPI is approximately constant and extends over ∼6 × 8 mm(2). This large and invariant CPI ensures a uniform representation of each point in visual space, with a complete representation of all visual features in V1, as originally proposed by Hubel and Wiesel (1974). In addition, we found several novel and unexpected asymmetries and anisotropies in the shapes of the CPI and the population receptive field. These results expand our understanding of the representation of visual space in V1 and are likely to be relevant for the representations of stimuli in other sensory cortical areas.

The relationship between voltage-sensitive dye imaging signals and spiking activity of neural populations in primate V1

Voltage-sensitive dye imaging (VSDI) is a powerful technique for measuring neural population responses from a large cortical region simultaneously with millisecond temporal resolution and columnar spatial resolution. However, the relationship between the average VSDI signal and the average spiking activity of neural populations is largely unknown. To better understand this relationship, we compared visual responses measured from V1 of behaving monkeys using VSDI and single-unit electrophysiology. We found large and systematic differences between position and orientation tuning properties obtained with these two techniques. We then determined that a simple computational model could explain these tuning differences. This model, together with our experimental results, allowed us to estimate the quantitative relationship between the average VSDI signal and local spiking activity. We found that this relationship is similar to the previously reported nonlinear relationship between average membrane potential and spike rate in single V1 neurons, suggesting that VSDI signals are dominated by subthreshold synaptic activity. This model, together with the VSDI measured maps for spatial position (retinotopy) and orientation, also allowed us to estimate the spatial integration area over which neural responses contribute to the VSDI signal at a given location. We found that the VSDI-integration area is consistent with a Gaussian envelope with a space constant of ∼230 μm. Finally, we show how this model and estimated parameters can be used to predict the pattern of population responses at the level of spiking activity from VSDI responses.

An illusion predicted by V1 population activity implicates cortical topography in shape perception

Mammalian primary visual cortex (V1) is topographically organized such that the pattern of neural activation in V1 reflects the location and spatial extent of visual elements in the retinal image, but it is unclear whether this organization contributes to visual perception. We combined computational modeling, voltage-sensitive dye imaging (VSDI) in behaving monkeys and behavioral measurements in humans to investigate whether the large-scale topography of V1 population responses influences shape judgments. Specifically, we used a computational model to design visual stimuli that had the same physical shape, but were predicted to elicit variable V1 response spread. We confirmed these predictions with VSDI. Finally, we designed a behavioral task in which human observers judged the shapes of these stimuli and found that their judgments were systematically distorted by the spread of V1 activity. This illusion suggests that the topographic pattern of neural population responses in visual cortex contributes to visual perception.

Calcium imaging with genetically encoded indicators in behaving primates

Understanding the neural basis of behaviour requires studying brain activity in behaving subjects using complementary techniques that measure neural responses at multiple spatial scales, and developing computational tools for understanding the mapping between these measurements. Here we report the first results of widefield imaging of genetically encoded calcium indicator (GCaMP6f) signals from V1 of behaving macaques. This technique provides a robust readout of visual population responses at the columnar scale over multiple mm2 and over several months. To determine the quantitative relation between the widefield GCaMP signals and the locally pooled spiking activity, we developed a computational model that sums the responses of V1 neurons characterized by prior single unit measurements. The measured tuning properties of the GCaMP signals to stimulus contrast, orientation and spatial position closely match the predictions of the model, suggesting that widefield GCaMP signals are linearly related to the summed local spiking activity. DOI: http://dx.doi.org/10.7554/eLife.16178.001

Inconsistencies between simultaneously measured neural and behavioral sensitivities in monkeys performing a fine orientation discrimination task

Neurons in the primate primary visual cortex (V1) are well organized into columns based on their orientation preference. Although this columnar organization has been studied for decades, it is still unclear how neural activity in these columns contributes to orientation perception. As a first step towards addressing this question, we used voltage sensitive dye imaging (VSDI) to measure the columnar signals in V1 of a monkey while it performed a fine orientation discrimination task. To assess neural sensitivity, we developed a linear decoder that pools the single-trial VSDI signals over space using weights proportional to the orientation response map at the columnar scale. The decoder then uses the pooled signals to perform the same task as the monkey. To test the hypothesis that columnar signals provide the main source of information in the orientation discrimination task, we varied the spatial frequency of the stimulus. Under this hypothesis, spatial frequency should have a similar effect on neural and behavioral sensitivities. However, we found systematic differences in the neural and behavioral effects of spatial frequency. The monkey was most sensitive at the middle frequency (2cpd), and performed worse at both low (0.5cpd) and high (8cpd) frequencies. In contrast, neural sensitivity was best at 8cpd, intermediate at 2cpd, and much worse at 0.5cpd. There are at least two possible sources that could contribute to these discrepancies between the neural and behavioral effects. First, additional sources of information beyond the columnar signals may contribute to behavioral performance at intermediate and low spatial frequencies. Second, the efficiency with which V1 signals at the columnar scale are decoded by subsequent processing stages may decrease with increasing spatial frequency. By studying trial-by-trial covariations between V1 signals and behavioral choices we may be able to distinguish between these possibilities. Meeting abstract presented at VSS 2015.