The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex

Mechanisms of Orientation Selectivity in the Primary Visual Cortex

The mechanisms underlying the emergence of orientation selectivity in the visual cortex have been, and continue to be, the subjects of intense scrutiny. Orientation selectivity reflects a dramatic change in the representation of the visual world: Whereas afferent thalamic neurons are generally orientation insensitive, neurons in the primary visual cortex (V1) are extremely sensitive to stimulus orientation. This profound change in the receptive field structure along the visual pathway has positioned V1 as a model system for studying the circuitry that underlies neural computations across the neocortex. The neocortex is characterized anatomically by the relative uniformity of its circuitry despite its role in processing distinct signals from region to region. A combination of physiological, anatomical, and theoretical studies has shed some light on the circuitry components necessary for generating orientation selectivity in V1. This targeted effort has led to critical insights, as well as controversies, concerning how neural circuits in the neocortex perform computations.

The Magnitude of Trial-By-Trial Neural Variability Is Reproducible over Time and across Tasks in Humans

Numerous studies have shown that neural activity in sensory cortices is remarkably variable over time and across trials even when subjects are presented with an identical repeating stimulus or task. This trial-by-trial neural variability is relatively large in the prestimulus period and considerably smaller (quenched) following stimulus presentation. Previous studies have suggested that the magnitude of neural variability affects behavior such that perceptual performance is better on trials and in individuals where variability quenching is larger. To what degree are neural variability magnitudes of individual subjects flexible or static? Here, we used EEG recordings from adult humans to demonstrate that neural variability magnitudes in visual cortex are remarkably consistent across different tasks and recording sessions. While magnitudes of neural variability differed dramatically across individual subjects, they were surprisingly stable across four tasks with different stimuli, temporal structures, and attentional/cognitive demands as well as across experimental sessions separated by one year. These experiments reveal that, in adults, neural variability magnitudes are mostly solidified individual characteristics that change little with task or time, and are likely to predispose individual subjects to exhibit distinct behavioral capabilities.

Orientation Tuning of Correlated Activity in the Developing Lateral Geniculate Nucleus

Neural circuits and the cells that comprise them undergo developmental changes in the spatial organization of their connections and in their temporal response properties. Within the lateral geniculate nucleus (LGN) of the dorsal thalamus, these changes have pronounced effects on the spatiotemporal receptive fields (STRFs) of neurons. An open and unresolved question is how STRF maturation affects stimulus-evoked correlated activity between pairs of LGN neurons during development. This is an important question to answer because stimulus-evoked correlated activity likely plays a role in establishing the specificity of thalamocortical connectivity and the receptive fields (RFs) of postsynaptic cortical neurons. Using multielectrode recording methods and white noise stimuli, we recorded neural activity from ensembles of LGN neurons in cats across early development. As expected, there was a progressive maturation of the spatial and temporal properties of visual responses. Using drifting bar stimuli and cross-correlation analysis, we also determined the orientation-tuning bandwidth of correlated activity between pairs of LGN neurons at different stages of development (Sillito and Jones, 2002; Andolina et al., 2007; Stanley et al., 2012; Kelly et al., 2014). Despite the larger RFs and slower responses of immature LGN neurons compared with mature neurons, our results show that correlated activity in the LGN was as tightly tuned for orientation early in development as it was in the adult. Closer examination revealed this age-invariant orientation tuning of correlated activity likely involves cellular mechanisms related to spike fatigue in young animals and a progressive decrease in response latency with development.SIGNIFICANCE STATEMENT Orientation tuning is a fundamental property of neurons in primary visual cortex. An important and unresolved question is how orientation tuning emerges during brain development. This study explores a potential mechanism for the establishment of orientation tuning based on correlated activity patterns among ensembles of maturing neurons in the lateral geniculate nucleus (LGN) of the thalamus. Results show that correlated activity between pairs of LGN neurons is more tightly tuned than predictions based simply on receptive field size, indicating that correlated activity has the properties needed to play an important role in the development of geniculocortical circuits and the emergence of cortical orientation tuning.

Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width

Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENT It is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single units and perception. Encoding models offer a way to bridge this gap by explicitly interpreting population activity as the aggregate response of many single neurons with known tuning properties. Here we use this approach to examine contrast-invariant orientation tuning of human V1. We show with experiment and modeling that due to lower signal to noise, contrast-invariant orientation tuning of single units manifests in population response functions that broaden at lower contrast, rather than remain contrast-invariant. These results highlight the need for explicit quantitative modeling when making a reverse inference from population response profiles to single-unit responses.

Weighted parallel contributions of binocular correlation and match signals to conscious perception of depth

Binocular disparity is detected in the primary visual cortex by a process similar to calculation of local cross-correlation between left and right retinal images. As a consequence, correlation-based neural signals convey information about false disparities as well as the true disparity. The false responses in the initial disparity detectors are eliminated at later stages in order to encode only disparities of the features correctly matched between the two eyes. For a simple stimulus configuration, a feed-forward nonlinear process can transform the correlation signal into the match signal. For human observers, depth judgement is determined by a weighted sum of the correlation and match signals rather than depending solely on the latter. The relative weight changes with spatial and temporal parameters of the stimuli, allowing adaptive recruitment of the two computations under different visual circumstances. A full transformation from correlation-based to match-based representation occurs at the neuronal population level in cortical area V4 and manifests in single-neuron responses of inferior temporal and posterior parietal cortices. Neurons in area V5/MT represent disparity in a manner intermediate between the correlation and match signals. We propose that the correlation and match signals in these areas contribute to depth perception in a weighted, parallel manner.This article is part of the themed issue 'Vision in our three-dimensional world'.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

Neural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.

Visual stimulation quenches global alpha range activity in awake primate V4: a case study

Increasing evidence suggests that sensory stimulation not only changes the level of cortical activity with respect to baseline but also its structure. Despite having been reported in a multitude of conditions and preparations (for instance, as a quenching of intertrial variability, Churchland et al., 2010), such changes remain relatively poorly characterized. Here, we used optical imaging of voltage-sensitive dyes to explore, in V4 of an awake macaque, the spatiotemporal characteristics of both visually evoked and spontaneously ongoing neuronal activity and their difference. With respect to the spontaneous case, we detected a reduction in large-scale activity ([Formula: see text]) in the alpha range (5 to 12.5 Hz) during sensory inflow accompanied by a decrease in pairwise correlations. Moreover, the spatial patterns of correlation obtained during the different visual stimuli were on the average more similar one to another than they were to that obtained in the absence of stimulation. Finally, these observed changes in activity dynamics approached saturation already at very low stimulus contrasts, unlike the progressive, near-linear increase of the mean raw evoked responses over a wide range of contrast values, which could indicate a specific switching in the presence of a sensory inflow.

Sensory noise predicts divisive reshaping of receptive fields

In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Population activity statistics dissect subthreshold and spiking variability in V1

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations.NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity.

Neural Variability Quenching Predicts Individual Perceptual Abilities

Neural activity during repeated presentations of a sensory stimulus exhibits considerable trial-by-trial variability. Previous studies have reported that trial-by-trial neural variability is reduced (quenched) by the presentation of a stimulus. However, the functional significance and behavioral relevance of variability quenching and the potential physiological mechanisms that may drive it have been studied only rarely. Here, we recorded neural activity with EEG as subjects performed a two-interval forced-choice contrast discrimination task. Trial-by-trial neural variability was quenched by ∼40% after the presentation of the stimulus relative to the variability apparent before stimulus presentation, yet there were large differences in the magnitude of variability quenching across subjects. Individual magnitudes of quenching predicted individual discrimination capabilities such that subjects who exhibited larger quenching had smaller contrast discrimination thresholds and steeper psychometric function slopes. Furthermore, the magnitude of variability quenching was strongly correlated with a reduction in broadband EEG power after stimulus presentation. Our results suggest that neural variability quenching is achieved by reducing the amplitude of broadband neural oscillations after sensory input, which yields relatively more reproducible cortical activity across trials and enables superior perceptual abilities in individuals who quench more.

SIGNIFICANCE STATEMENT

Variability quenching is a phenomenon in which neural variability across trials is reduced by the presentation of a stimulus. Although this phenomenon has been reported across a variety of animal and human studies, its functional significance and behavioral relevance have been examined only rarely. Here, we report novel empirical evidence from humans revealing that variability quenching differs dramatically across individual subjects and explains to a certain degree why some individuals exhibit better perceptual abilities than others. In addition, we found a strong relationship between variability quenching and suppression of broadband neural oscillations. Together, our results reveal the importance of reproducible cortical activity for enabling better perceptual abilities and suggest a potential underlying mechanism that may explain why variability quenching occurs.

An Extended Normalization Model of Attention Accounts for Feature-Based Attentional Enhancement of Both Response and Coherence Gain

Paying attention to a sensory feature improves its perception and impairs that of others. Recent work has shown that a Normalization Model of Attention (NMoA) can account for a wide range of physiological findings and the influence of different attentional manipulations on visual performance. A key prediction of the NMoA is that attention to a visual feature like an orientation or a motion direction will increase the response of neurons preferring the attended feature (response gain) rather than increase the sensory input strength of the attended stimulus (input gain). This effect of feature-based attention on neuronal responses should translate to similar patterns of improvement in behavioral performance, with psychometric functions showing response gain rather than input gain when attention is directed to the task-relevant feature. In contrast, we report here that when human subjects are cued to attend to one of two motion directions in a transparent motion display, attentional effects manifest as a combination of input and response gain. Further, the impact on input gain is greater when attention is directed towards a narrow range of motion directions than when it is directed towards a broad range. These results are captured by an extended NMoA, which either includes a stimulus-independent attentional contribution to normalization or utilizes direction-tuned normalization. The proposed extensions are consistent with the feature-similarity gain model of attention and the attentional modulation in extrastriate area MT, where neuronal responses are enhanced and suppressed by attention to preferred and non-preferred motion directions respectively.

We report a pattern of feature-based attentional effects on human psychophysical performance, which cannot be accounted for by the Normalization Model of Attention using biologically plausible parameters. Specifically, this prominent model of attentional modulation predicts that attention to a visual feature like a specific motion direction will lead to a response gain in the input-response function, rather than the input gain that we actually observe. In our data, the input gain is greater when attention is directed towards a narrow range of motion directions, again contrary to the model’s prediction. We therefore propose two physiologically testable extensions of the model that include direction-tuned normalization mechanisms of attention. Both extensions account for our data without affecting the previously demonstrated successful performance of the NMoA.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

Orientation selectivity in rat primary visual cortex emerges earlier with low-contrast and high-luminance stimuli

In natural vision, rapid and sustained variations in luminance and contrast change the reliability of information available about a visual scene, and markedly affect both neuronal and behavioural responses. The hallmark property of neurons in primary visual cortex (V1), orientation selectivity, is unaffected by changes in stimulus contrast, but it remains unclear how sustained differences in mean luminance and contrast affect the time-course of orientation selectivity, and the amount of information that neurons carry about orientation. We used reverse correlation with characterize the temporal dynamics of orientation selectivity in rat V1 neurons under four luminance-contrast conditions. We show that orientation selectivity and mutual information between neuronal responses and stimulus orientation are invariant to contrast or mean luminance. Critically, the time-course of the emergence of orientation selectivity was affected by both factors; response latencies were longer for low- than high-luminance gratings, and surprisingly, response latencies were also longer for high- than low-contrast gratings. Modelling suggests that luminance-modulated changes in feedforward gain, in combination with hyperpolarization caused by high contrasts can account for our physiological data. The hyperpolarization at high contrasts may increase signal-to-noise ratios, whereas a more depolarized membrane may lead to greater sensitivity to weak stimuli.

Functional dissection of inhibitory microcircuits in the visual cortex

Cerebral cortex contains various types of GABAergic neurons exerting local inhibition. Although the number of GABAergic inhibitory neurons is much smaller than glutamatergic excitatory neurons, they show greater diversity in their morphological and physiological properties. Genetic markers for distinct sub-classes of GABAergic neurons have been identified, and technical advances achieved in the past few decades have brought about a demonstration of a unique function of each sub-class of GABAergic neurons in the cortex. In particular, visual processing in the cortex requires inhibitory function of various GABAergic neurons. Here, we summarize current understandings on the function of inhibitory neurons in the cortex, especially focusing on their roles in visual processing.

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder

View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

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

An important question in brain research is how neurons and the circuits they form process information to produce behavior. To understand what happens in a human brain, it is necessary to study a brain of similar complexity, such as that of a primate. Examining how the neurons in a brain region called the visual cortex process information about what we see is especially informative. This is because animals can be taught to perform different visual tasks, and because the visual cortex is relatively easy to access. In principle, therefore, it should be possible to use modern genetic and imaging techniques to study the primate visual system, but, until now, that has not been the case.

Like much of the brain, the visual cortex consists of different classes of neurons that can excite, inhibit or modulate the activity of neighboring neurons. One way to study how these different classes of neurons interact with each other is to alter the animal’s DNA, such that only one cell type stands out during the experiment, allowing its role in the brain to be closely monitored. This technique has been used to study the interactions among neurons in the rodent brain, because rodent DNA is easy to alter. However, it is not easy to manipulate primate DNA.

Seidemann et al. have, therefore, developed a new technique that can target a specific class of neurons, allowing the activity of just these cells to be distinguished from the rest. The method uses specially designed harmless viruses to produce foreign proteins in the excitatory neurons of the visual cortex in an adult macaque. The optical properties of the proteins change when the neuron they are in is active, allowing the activity of the excitatory neurons to be detected and tracked in awake animals while they perform a visual task.

Previously, the activity of neurons in the primate visual cortex could only be measured using dyes that indiscriminately reported the activity of all the neurons present. Seidemann et al. found that, in addition to being more selective than the dye-based method, the new technique also more accurately depicted neuronal action potentials, which are the primary units of information in the brain.

Seidemann et al. now plan to use a similar method to study the activity of the inhibitory neurons of the primate visual cortex. Further examination of both excitatory and inhibitory neurons at much higher magnification, using a different microscopy technique, will also reveal more subtle features of their responses during visual tasks.

DOI:http://dx.doi.org/10.7554/eLife.16178.002

Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information

Numerous studies have shown that neuronal responses are modulated by stimulus properties and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population activity in primate visual cortex modulate the tuning of neurons in a multiplicative and additive manner. While distributed on a continuum, neurons with stronger multiplicative effects tended to have less additive modulation and vice versa. The information encoded by multiplicatively modulated neurons increased with greater population activity, while that of additively modulated neurons decreased. These effects offset each other so that population activity had little effect on total information. Our results thus suggest that intrinsic activity fluctuations may act as a "traffic light" that determines which subset of neurons is most informative.

Canonical computations of cerebral cortex

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical input largely, but not completely, cancels this external input. This operation leads to many characteristic cortical nonlinearities in integrating multiple stimuli. The cortical computation must combine such local processing with hierarchical processing across areas. We point to important changes in moving from sensory cortex to motor and frontal cortex and the possibility of substantial differences between cortex in rodents vs. species with columnar organization of selectivity.

Pooled, but not single-neuron, responses in macaque V4 represent a solution to the stereo correspondence problem

Binocular disparity is an important cue for depth perception. To correctly represent disparity, neurons must find corresponding visual features between the left- and right-eye images. The visual pathway ascending from V1 to inferior temporal cortex solves the correspondence problem. An intermediate area, V4, has been proposed to be a critical stage in the correspondence process. However, the distinction between V1 and V4 is unclear, because accumulating evidence suggests that the process begins within V1. In this article, we report that the pooled responses in macaque V4, but not responses of individual neurons, represent a solution to the correspondence problem. We recorded single-unit responses of V4 neurons to random-dot stereograms of varying degrees of anticorrelation. To achieve gradual anticorrelation, we reversed the contrast of an increasing proportion of dots as in our previous psychophysical studies, which predicted that the neural correlates of the solution to correspondence problem should gradually eliminate their disparity modulation as the level of anticorrelation increases. Inconsistent with this prediction, the tuning amplitudes of individual V4 neurons quickly decreased to a nonzero baseline with small anticorrelation. By contrast, the shapes of individual tuning curves changed more gradually so that the amplitude of population-pooled responses gradually decreased toward zero over the entire range of graded anticorrelation. We explain these results by combining multiple energy-model subunits. From a comparison with the population-pooled responses in V1, we suggest that disparity representation in V4 is distinctly advanced from that in V1. Population readout of V4 responses provides disparity information consistent with the correspondence solution.

On the Effects on Cortical Spontaneous Activity of the Symmetries of the Network of Pinwheels in Visual Area V1

This paper challenges and extends earlier seminal work. We consider the problem of describing mathematically the spontaneous activity of V1 by combining several important experimental observations including (1) the organization of the visual cortex into a spatially periodic network of hypercolumns structured around pinwheels, (2) the difference between short-range and long-range intracortical connections, the first ones being rather isotropic and producing naturally doubly periodic patterns by Turing mechanisms, the second one being patchy, and (3) the fact that the Turing patterns spontaneously produced by the short-range connections and the network of pinwheels have similar periods. By analyzing the PO maps, we are able to classify all possible singular points (the pinwheels) as having symmetries described by a small subset of the wallpaper groups. We then propose a description of the spontaneous activity of V1 using a classical voltage-based neural field model that features isotropic short-range connectivities modulated by non-isotropic long-range connectivities. A key observation is that, with only short-range connections and because the problem has full translational invariance in this case, a spontaneous doubly periodic pattern generates a 2-torus in a suitable functional space which persists as a flow-invariant manifold under small perturbations, for example when turning on the long-range connections. Through a complete analysis of the symmetries of the resulting neural field equation and motivated by a numerical investigation of the bifurcations of their solutions, we conclude that the branches of solutions which are stable over an extended range of parameters are those that correspond to patterns with an hexagonal (or nearly hexagonal) symmetry. The question of which patterns persist when turning on the long-range connections is answered by (1) analyzing the remaining symmetries on the perturbed torus and (2) combining this information with the Poincaré-Hopf theorem. We have developed a numerical implementation of the theory that has allowed us to produce the predicted patterns of activities, the planforms. In particular we generalize the contoured and non-contoured planforms predicted by previous authors.

Functional characterization of spikelet activity in the primary visual cortex

KEY POINTS

In vivo whole-cell patch-clamp recordings in cat visual cortex revealed small deflections in the membrane potential of neurons, termed spikelets. Spikelet statistics and functional properties suggest these deflections originate from a single, nearby cell. Spikelets shared a number sensory selectivities with the principal neuron including orientation selectivity, receptive field location and eye preference. Principal neurons and spikelets did not, however, generally share preferences for depth (binocular disparity). Cross-correlation of spikelet activity and membrane potential revealed direct effects on the membrane potential of some principal neurons, suggesting that these cells were synaptically coupled or received common input from the cortical network. Other spikelet-neuron pairs revealed indirect effects, likely to be the result of correlated network events.

ABSTRACT

Intracellular recordings in the neocortex reveal not only the membrane potential of neurons, but small unipolar or bipolar deflections that are termed spikelets. Spikelets have been proposed to originate from various sources, including active dendritic mechanisms, gap junctions and extracellular signals. Here we examined the functional characteristics of spikelets measured in neurons from cat primary visual cortex in vivo. Spiking statistics and our functional characterization of spikelet activity indicate that spikelets originate from a separate, nearby cell. Spikelet kinetics and lack of a direct effect on spikelet activity from hyperpolarizing current injection suggest they do not arise from electrical coupling to the principal neuron being recorded. Spikelets exhibited matched orientation tuning preference and ocular dominance to the principal neuron. In contrast, binocular disparity preferences of spikelets and the principal neuron were unrelated. Finally, we examined the impact of spikelets on the principal neuron's membrane potential; we did observe some records for which spikelets were correlated with the membrane potential of the principal neuron, suggesting that these neurons were synaptically coupled or received common input from the cortical network.

Vibrissa Self-Motion and Touch Are Reliably Encoded along the Same Somatosensory Pathway from Brainstem through Thalamus

Active sensing involves the fusion of internally generated motor events with external sensation. For rodents, active somatosensation includes scanning the immediate environment with the mystacial vibrissae. In doing so, the vibrissae may touch an object at any angle in the whisk cycle. The representation of touch and vibrissa self-motion may in principle be encoded along separate pathways, or share a single pathway, from the periphery to cortex. Past studies established that the spike rates in neurons along the lemniscal pathway from receptors to cortex, which includes the principal trigeminal and ventral-posterior-medial thalamic nuclei, are substantially modulated by touch. In contrast, spike rates along the paralemniscal pathway, which includes the rostral spinal trigeminal interpolaris, posteromedial thalamic, and ventral zona incerta nuclei, are only weakly modulated by touch. Here we find that neurons along the lemniscal pathway robustly encode rhythmic whisking on a cycle-by-cycle basis, while encoding along the paralemniscal pathway is relatively poor. Thus, the representations of both touch and self-motion share one pathway. In fact, some individual neurons carry both signals, so that upstream neurons with a supralinear gain function could, in principle, demodulate these signals to recover the known decoding of touch as a function of vibrissa position in the whisk cycle.

Contrast invariance of orientation tuning in cat primary visual cortex neurons depends on stimulus size

KEY POINTS

The process of orientation tuning is an important and well-characterized feature of neurons in primary visual cortex. The combination of ascending and descending circuits involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model of orientation tuning predicts a broadening effect due to increasing contrast; yet, experimental results consistently report contrast invariance. We show here that contrast invariance actually depends on stimulus size such that large stimuli extending beyond the neuron's receptive field engage circuits that promote invariance, whereas optimally sized, smaller stimuli result in contrast variance that is more in line with the classical orientation tuning model. These results illustrate the importance of optimizing stimulus parameters to best reflect the sensory pathways under study and provide new clues about different circuits that may be involved in variant and invariant response properties.

ABSTRACT

Selective response to stimulus orientation is a key feature of neurons in primary visual cortex, yet the underlying mechanisms generating orientation tuning are not fully understood. The combination of feed-forward and cortical mechanisms involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model predicts that orientation tuning should broaden considerably with increasing contrast; however, experimental results consistently report contrast invariance. We show here, in primary visual cortex of anaesthetized cats under neuromuscular blockade, that contrast invariance occurs when visual stimuli are large enough to include the extraclassical surround (ECS), which is likely to involve circuits of suppression that may not be entirely feed-forward in origin. On the other hand, when stimulus size is optimized to the classical receptive field of each neuron, the population average shows a statistically significant 40% increase in tuning width at high contrast, demonstrating that contrast variance of orientation tuning can occur. Conversely, our results also suggest that the phenomenon of contrast invariance relies in part on the presence of the ECS. Moreover, these results illustrate the importance of optimizing stimulus parameters to best reflect the neural pathways under study.

The Nature of Shared Cortical Variability

Neuronal responses of sensory cortex are highly variable, and this variability is correlated across neurons. To assess how variability reflects factors shared across a neuronal population, we analyzed the activity of many simultaneously recorded neurons in visual cortex. We developed a simple model that comprises two sources of shared variability: a multiplicative gain, which uniformly scales each neuron’s sensory drive, and an additive offset, which affects different neurons to different degrees. This model captured the variability of spike counts and reproduced the dependence of pairwise correlations on neuronal tuning and stimulus orientation. The relative contributions of the additive and multiplicative fluctuations could vary over time and had marked impact on population coding. These observations indicate that shared variability of neuronal populations in sensory cortex can be largely explained by two factors that modulate the whole population.

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Response variability in V1 neuronal populations is largely shared across neurons

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Shared variability involves two factors: a multiplicative gain and an additive offset

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These two factors predict sensory responses of large populations on single trials

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They determine pairwise correlations and constrain information coding

Origins of feature selectivities and maps in the mammalian primary visual cortex

A common feature of the mammalian striate cortex is the arrangement of 'orientation domains' containing neurons preferring similar stimulus orientations. They are arranged as spokes of a pinwheel that converge at singularities known as 'pinwheel centers'. We propose that a cortical network of feedforward and intracortical lateral connections elaborates a full set of optimum orientations from geniculate inputs that show a bias to stimulus orientation and form a set of two or a small number of 'Cartesian' coordinates. Because each geniculate afferent carries signals only from one eye and its receptive field (RF) is either ON or OFF center, the network constructs also ocular dominance columns and a quasi-segregation of ON and OFF responses across the horizontal extent of the striate cortex.

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.

In primary visual cortex of mammals, neurons are selective to the orientation of contrast edges. In some species, as cats and monkeys, neurons preferring similar orientations are adjacent on the cortical surface, leading to smooth orientation maps. In rodents, in contrast, such spatial orientation maps do not exist, and neurons of different specificities are mixed in a salt-and-pepper fashion. During development, however, a “functional” map of orientation selectivity emerges, where connections between neurons of similar preferred orientations are selectively enhanced. Here we show how such feature-specific connectivity can arise in realistic neocortical networks of excitatory and inhibitory neurons. Our results demonstrate how recurrent dynamics can work in cooperation with synaptic plasticity to form networks where neurons preferring similar stimulus features connect more strongly together. Such networks, in turn, are known to enhance the specificity of neuronal responses to a stimulus. Our study thus reveals how self-organizing connectivity in neuronal networks enable them to achieve new or enhanced functions, and it underlines the essential role of recurrent inhibition and plasticity in this process.

Primary visual cortex represents the difference between past and present

The visual system is confronted with rapidly changing stimuli in everyday life. It is not well understood how information in such a stream of input is updated within the brain. We performed voltage-sensitive dye imaging across the primary visual cortex (V1) to capture responses to sequences of natural scene contours. We presented vertically and horizontally filtered natural images, and their superpositions, at 10 or 33 Hz. At low frequency, the encoding was found to represent not the currently presented images, but differences in orientation between consecutive images. This was in sharp contrast to more rapid sequences for which we found an ongoing representation of current input, consistent with earlier studies. Our finding that for slower image sequences, V1 does no longer report actual features but represents their relative difference in time counteracts the view that the first cortical processing stage must always transfer complete information. Instead, we show its capacities for change detection with a new emphasis on the role of automatic computation evolving in the 100-ms range, inevitably affecting information transmission further downstream.

Weak orientation and direction selectivity in lateral geniculate nucleus representing central vision in the gray squirrel Sciurus carolinensis

Classic studies of lateral geniculate nucleus (LGN) and visual cortex (V1) in carnivores and primates have found that a majority of neurons in LGN exhibit a center-surround organization, while V1 neurons exhibit strong orientation selectivity and, in many species, direction selectivity. Recent work in the mouse and the monkey has discovered previously unknown classes of orientation- and direction-selective neurons in LGN. Furthermore, some recent studies in the mouse report that many LGN cells exhibit pronounced orientation biases that are of comparable strength to the subthreshold inputs to V1 neurons. These results raise the possibility that, in rodents, orientation biases of individual LGN cells make a substantial contribution to cortical orientation selectivity. Alternatively, the size and contribution of orientation- or direction-selective channels from LGN to V1 may vary across mammals. To address this question, we examined orientation and direction selectivity in LGN and V1 neurons of a highly visual diurnal rodent: the gray squirrel. In the representation of central vision, only a few LGN neurons exhibited strong orientation or direction selectivity. Across the population, LGN neurons showed weak orientation biases and were much less selective for orientation compared with V1 neurons. Although direction selectivity was weak overall, LGN layers 3abc, which contain neurons that express calbindin, exhibited elevated direction selectivity index values compared with LGN layers 1 and 2. These results suggest that, for central visual fields, the contribution of orientation- and direction-selective channels from the LGN to V1 is small in the squirrel. As in other mammals, this small contribution is elevated in the calbindin-positive layers of the LGN.

The effects of short-term synaptic depression at thalamocortical synapses on orientation tuning in cat V1

We examine the effects of short-term synaptic depression on the orientation tuning of the LGN input to simple cells in cat primary visual cortex (V1). The total LGN input has an untuned component as well as a tuned component, both of which grow with stimulus contrast. The untuned component is not visible in the firing rate responses of the simple cells. The suppression of the contribution of the untuned input component to firing rate responses is key to establishing orientation selectivity and its invariance with stimulus contrast. It has been argued that synaptic depression of LGN inputs could contribute to the selective suppression of the untuned component and thus contribute to the tuning observed in simple cells. We examine this using a model fit to the depression observed at thalamocortical synapses in-vivo, and compare this to an earlier model fit based on in-vitro observations. We examine the tuning of both the conductance and the firing rate induced in simple cells by the net LGN input. We find that depression causes minimal suppression of the untuned component. The primary effect of depression is to cause the contrast response curve to saturate at lower contrasts without differentially affecting the tuned vs. untuned components. This effect is slightly weaker for in-vivo vs. in-vitro parameters. Thus, synaptic depression of LGN inputs does not appreciably contribute to the orientation tuning of V1 simple cells.

A review of the mechanisms by which attentional feedback shapes visual selectivity

The glut of information available for the brain to process at any given moment necessitates an efficient attentional system that can 'pick and choose' what information receives prioritized processing. A growing body of work, spanning numerous methodologies and species, reveals that one powerful way in which attending to an item separates the wheat from the chaff is by altering a basic response property in the brain: neuronal selectivity. Selectivity is a cornerstone response property, largely dictating our ability to represent and interact with the environment. Although it is likely that selectivity is altered throughout many brain areas, here we focus on how directing attention to an item affects selectivity in the visual system, where this response property is generally more well characterized. First, we review the neural architecture supporting selectivity, and then discuss the various changes that could occur in selectivity for an attended item. In a survey of the literature, spanning neurophysiology, neuroimaging and psychophysics, we reveal that there is general convergence regarding the manner with which selectivity is shaped by attentional feedback. In a nutshell, the literature suggests that the type of changes in selectivity that manifest appears to depend on the type of attention being deployed: whereas directing spatial attention towards an item only alters spatial selectivity, directing feature-based attention can alter the selectivity of attended features.

Intracellular correlates of stimulus-specific adaptation

Stimulus-specific adaptation (SSA) is the reduction in response to a common stimulus that does not generalize, or only partially generalizes, to rare stimuli. SSA is strong and widespread in primary auditory cortex (A1) of rats, but is weak or absent in the main input station to A1, the ventral division of the medial geniculate body. To study SSA in A1, we recorded neural activity in A1 intracellularly using sharp electrodes. We studied the responses to tone pips of the same frequency in different contexts: as Standard and Deviants in Oddball sequences; in equiprobable sequences; in sequences consisting of rare tone presentations; and in sequences composed of many different frequencies, each of which was rare. SSA was found both in subthreshold membrane potential fluctuations and in spiking responses of A1 neurons. SSA for changes in frequency was large at a frequency difference of 44% between Standard and Deviant, and clearly present with tones separated by as little as 4%, near the behavioral frequency difference limen in rats. When using equivalent measures, SSA in spiking responses was generally larger than the SSA at the level of the membrane potential. This effect can be traced to the nonlinearity of the transformation between membrane potential to spikes. Using the responses to the same tone in different contexts made it possible to demonstrate that cortical SSA could not be fully explained by adaptation in narrow frequency channels, even at the level of the membrane potential. We conclude that local processing significantly contributes to the generation of cortical SSA.

Mean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons

Mechanisms underlying the emergence of orientation selectivity in the primary visual cortex are highly debated. Here we study the contribution of inhibition-dominated random recurrent networks to orientation selectivity, and more generally to sensory processing. By simulating and analyzing large-scale networks of spiking neurons, we investigate tuning amplification and contrast invariance of orientation selectivity in these networks. In particular, we show how selective attenuation of the common mode and amplification of the modulation component take place in these networks. Selective attenuation of the baseline, which is governed by the exceptional eigenvalue of the connectivity matrix, removes the unspecific, redundant signal component and ensures the invariance of selectivity across different contrasts. Selective amplification of modulation, which is governed by the operating regime of the network and depends on the strength of coupling, amplifies the informative signal component and thus increases the signal-to-noise ratio. Here, we perform a mean-field analysis which accounts for this process.

Genetic disruption of the On visual pathway affects cortical orientation selectivity and contrast sensitivity in mice

The retina signals stimulus contrast via parallel On and Off pathways and sends the information to higher visual centers. Here we study the role of the On pathway using mice that have null mutations in the On-specific GRM6 receptor in the retina (Pinto LH, Vitaterna MH, Shimomura K, Siepka SM, Balannik V, McDearmon EL, Omura C, Lumayag S, Invergo BM, Brandon M, Glawe B, Cantrell DR, Donald R, Inayat S, Olvera MA, Vessey KA, Kirstan A, McCall MA, Maddox D, Morgans CW, Young B, Pletcher MT, Mullins RF, Troy JB, Takahashi JS. Vis Neurosci 24: 111-123, 2007; Maddox DM, Vessey KA, Yarbrough GL, Invergo BM, Cantrell DR, Inayat S, Balannik V, Hicks WL, Hawes NL, Byers S, Smith RS, Hurd R, Howell D, Gregg RG, Chang B, Naggert JK, Troy JB, Pinto LH, Nishina PM, McCall MA. J Physiol 586: 4409-4424, 2008). In these "nob" mice, single unit recordings in the primary visual cortex (V1) reveal degraded selectivity for orientations due to an increased response at nonpreferred orientations. Contrast sensitivity in the nob mice is reduced with severe deficits at low contrast, consistent with the phenotype of night blindness in human patients with mutations in Grm6. These cortical deficits can be largely explained by reduced input drive and increased response variability seen in nob V1. Interestingly, increased variability is also observed in the superior colliculus of these mice but does not affect its tuning properties. Further, the increased response variability in the nob mice is traced to the retina, a result phenocopied by acute pharmacological blockade of the On pathway in wild-type retina. Together, our results suggest that the On and Off pathways normally interact to increase response reliability in the retina, which in turn propagates to various central visual targets and affects their functional properties.

The role of thalamic population synchrony in the emergence of cortical feature selectivity

In a wide range of studies, the emergence of orientation selectivity in primary visual cortex has been attributed to a complex interaction between feed-forward thalamic input and inhibitory mechanisms at the level of cortex. Although it is well known that layer 4 cortical neurons are highly sensitive to the timing of thalamic inputs, the role of the stimulus-driven timing of thalamic inputs in cortical orientation selectivity is not well understood. Here we show that the synchronization of thalamic firing contributes directly to the orientation tuned responses of primary visual cortex in a way that optimizes the stimulus information per cortical spike. From the recorded responses of geniculate X-cells in the anesthetized cat, we synthesized thalamic sub-populations that would likely serve as the synaptic input to a common layer 4 cortical neuron based on anatomical constraints. We used this synchronized input as the driving input to an integrate-and-fire model of cortical responses and demonstrated that the tuning properties match closely to those measured in primary visual cortex. By modulating the overall level of synchronization at the preferred orientation, we show that efficiency of information transmission in the cortex is maximized for levels of synchronization which match those reported in thalamic recordings in response to naturalistic stimuli, a property which is relatively invariant to the orientation tuning width. These findings indicate evidence for a more prominent role of the feed-forward thalamic input in cortical feature selectivity based on thalamic synchronization.

While the visual system is selective for a wide range of different inputs, orientation selectivity has been considered the preeminent property of the mammalian visual cortex. Existing models of this selectivity rely on varying relative importance of feedforward thalamic input and intracortical influence. Recently, we have shown that pairwise timing relationships between single thalamic neurons can be predictive of a high degree of orientation selectivity. Here we have constructed a computational model that predicts cortical orientation tuning from thalamic populations. We show that this arrangement, relying on precise timing differences between thalamic responses, accurately predicts tuning properties as well as demonstrates that certain timing relationships are optimal for transmitting information about the stimulus to cortex.

Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex

Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing rates. Together these two mechanisms, thought to be applicable across sensory systems in general, lead to biological maps that develop stably and robustly, yet adapt to the visual environment. The modeling results suggest that topographic map stability is a natural outcome of low-level processes of adaptation and normalization. The resulting model is more realistic, simpler, and far more robust, and is thus a good starting point for future studies of cortical map development.

Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

The human brain is notoriously “noisy”. Even with identical physical sensory inputs and task demands, brain responses and behavioral output vary tremendously from trial to trial. Such brain and behavioral variability and the relationship between them have been the focus of intense neuroscience research for decades. Traditionally, it is thought that the relationship between trial-to-trial brain activity and behavioral performance is monotonic: the highest or lowest brain activity levels are associated with the best behavioral performance. Using invasive recordings in neurosurgical patients, we demonstrate an inverted-U relationship between brain and behavioral variability. Under such a relationship, moderate brain activity is associated with the best performance, while both very low and very high brain activity levels are predictive of compromised performance. These results have significant implications for our understanding of brain functioning. They further support recent theoretical frameworks that view the brain as an active nonlinear dynamical system instead of a passive signal-processing device.

Orientation-selective responses in the mouse lateral geniculate nucleus

The dorsal lateral geniculate nucleus (dLGN) receives visual information from the retina and transmits it to the cortex. In this study, we made extracellular recordings in the dLGN of both anesthetized and awake mice, and found that a surprisingly high proportion of cells were selective for stimulus orientation. The orientation selectivity of dLGN cells was unchanged after silencing the visual cortex pharmacologically, indicating that it is not due to cortical feedback. The orientation tuning of some dLGN cells correlated with their elongated receptive fields, while in others orientation selectivity was observed despite the fact that their receptive fields were circular, suggesting that their retinal input might already be orientation selective. Consistently, we revealed orientation/axis-selective ganglion cells in the mouse retina using multielectrode arrays in an in vitro preparation. Furthermore, the orientation tuning of dLGN cells was largely maintained at different stimulus contrasts, which could be sufficiently explained by a simple linear feedforward model. We also compared the degree of orientation selectivity in different visual structures under the same recording condition. Compared with the dLGN, orientation selectivity is greatly improved in the visual cortex, but is similar in the superior colliculus, another major retinal target. Together, our results demonstrate prominent orientation selectivity in the mouse dLGN, which may potentially contribute to visual processing in the cortex.

Effects of stimulus spatial frequency, size, and luminance contrast on orientation tuning of neurons in the dorsal lateral geniculate nucleus of cat

It is generally thought that orientation selectivity first appears in the primary visual cortex (V1), whereas neurons in the lateral geniculate nucleus (LGN), an input source for V1, are thought to be insensitive to stimulus orientation. Here we show that increasing both the spatial frequency and size of the grating stimuli beyond their respective optimal values strongly enhance the orientation tuning of LGN neurons. The resulting orientation tuning was clearly contrast-invariant. Furthermore, blocking intrathalamic inhibition by iontophoretically administering γ-aminobutyric acid (GABA)A receptor antagonists, such as bicuculline and GABAzine, slightly but significantly weakened the contrast invariance. Our results suggest that orientation tuning in the LGN is caused by an elliptical classical receptive field and orientation-tuned surround suppression, and that its contrast invariance is ensured by local GABAA inhibition. This contrast-invariant orientation tuning in LGN neurons may contribute to the contrast-invariant orientation tuning seen in V1 neurons.

Learning contrast-invariant cancellation of redundant signals in neural systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system

Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.

Simple cells in the primary visual cortex (V1) demonstrate many response properties that are either nonlinear or involve response modulations (i.e., stimuli that do not cause a response in isolation alter the cell's response to other stimuli). These non-classical receptive field (nCRF) effects are generally modeled individually and their collective role in biological vision is not well understood. Previous work has shown that classical receptive field (CRF) properties of V1 cells (i.e., the spatial structure of the visual field responsive to stimuli) could be explained by the sparse coding hypothesis, which is an optimal coding model that conjectures a neural population should use the fewest number of cells simultaneously to represent each stimulus. In this paper, we have performed extensive simulated physiology experiments to show that many nCRF response properties are simply emergent effects of a dynamical system implementing this same sparse coding model. These results suggest that rather than representing disparate information processing operations themselves, these nCRF effects could be consequences of an optimal sensory coding strategy that attempts to represent each stimulus most efficiently. This interpretation provides a potentially unifying high-level functional interpretation to many response properties that have generally been viewed through distinct models.

Synchronisation hubs in the visual cortex may arise from strong rhythmic inhibition during gamma oscillations

Neurons in the visual cortex exhibit heterogeneity in feature selectivity and the tendency to generate action potentials synchronously with other nearby neurons. By examining visual responses from cat area 17 we found that, during gamma oscillations, there was a positive correlation between each unit's sharpness of orientation tuning, strength of oscillations, and propensity towards synchronisation with other units. Using a computational model, we demonstrated that heterogeneity in the strength of rhythmic inhibitory inputs can account for the correlations between these three properties. Neurons subject to strong inhibition tend to oscillate strongly in response to both optimal and suboptimal stimuli and synchronise promiscuously with other neurons, even if they have different orientation preferences. Moreover, these strongly inhibited neurons can exhibit sharp orientation selectivity provided that the inhibition they receive is broadly tuned relative to their excitatory inputs. These results predict that the strength and orientation tuning of synaptic inhibition are heterogeneous across area 17 neurons, which could have important implications for these neurons' sensory processing capabilities. Furthermore, although our experimental recordings were conducted in the visual cortex, our model and simulation results can apply more generally to any brain region with analogous neuron types in which heterogeneity in the strength of rhythmic inhibition can arise during gamma oscillations.

A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex

Cortical spontaneous activity reflects an animal's behavioral state and affects neural responses to sensory stimuli. The correlation between excitatory and inhibitory synaptic input to single neurons is a key parameter in models of cortical circuitry. Recent measurements demonstrated highly correlated synaptic excitation and inhibition during spontaneous "up-and-down" states, during which excitation accounted for approximately 80% of inhibitory variance (Shu et al., 2003; Haider et al., 2006). Here we report in vivo whole-cell estimates of the correlation between excitation and inhibition in the rat visual cortex under pentobarbital anesthesia, during which up-and-down states are absent. Excitation and inhibition are weakly correlated, relative to the up-and-down state: excitation accounts for less than 40% of inhibitory variance. Although these correlations are lower than when the circuit cycles between up-and-down states, both behaviors may arise from the same circuitry. Our observations provide evidence that different correlational patterns of excitation and inhibition underlie different cortical states.

Analysis of the stabilized supralinear network

We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear input-output function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong. For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs. We compare this to the dynamic stabilization in the balanced network, which yields only linear behavior. We more exhaustively analyze the two-dimensional case of one excitatory and one inhibitory population. We show that in this case, dynamic stabilization will occur whenever the determinant of the weight matrix is positive and the inhibitory time constant is sufficiently small, and analyze the conditions for supersaturation, or decrease of firing rates with increasing stimulus contrast (which represents increasing input firing rates). In work to be presented elsewhere, we have found that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.

Spontaneous and task-evoked brain activity negatively interact

A widely held assumption is that spontaneous and task-evoked brain activity sum linearly, such that the recorded brain response in each single trial is the algebraic sum of the constantly changing ongoing activity and the stereotypical evoked activity. Using functional magnetic resonance imaging signals acquired from normal humans, we show that this assumption is invalid. Across widespread cortices, evoked activity interacts negatively with ongoing activity, such that higher prestimulus baseline results in less activation or more deactivation. As a consequence of this negative interaction, trial-to-trial variability of cortical activity decreases following stimulus onset. We further show that variability reduction follows overlapping but distinct spatial pattern from that of task-activation/deactivation and it contains behaviorally relevant information. These results favor an alternative perspective to the traditional dichotomous framework of ongoing and evoked activity. That is, to view the brain as a nonlinear dynamical system whose trajectory is tighter when performing a task. Further, incoming sensory stimuli modulate the brain's activity in a manner that depends on its initial state. We propose that across-trial variability may provide a new approach to brain mapping in the context of cognitive experiments.

Optimal balance of the striatal medium spiny neuron network

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of 10 ~ 20% and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around 15% connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics - it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.

Reduced crowding and poor contour detection in schizophrenia are consistent with weak surround inhibition

BACKGROUND

Detection of visual contours (strings of small oriented elements) is markedly poor in schizophrenia. This has previously been attributed to an inability to group local information across space into a global percept. Here, we show that this failure actually originates from a combination of poor encoding of local orientation and abnormal processing of visual context.

METHODS

We measured the ability of observers with schizophrenia to localise contours embedded in backgrounds of differently oriented elements (either randomly oriented, near-parallel or near-perpendicular to the contour). In addition, we measured patients' ability to process local orientation information (i.e., report the orientation of an individual element) for both isolated and crowded elements (i.e., presented with nearby distractors).

RESULTS

While patients are poor at detecting contours amongst randomly oriented elements, they are proportionally less disrupted (compared to unaffected controls) when contour and surrounding elements have similar orientations (near-parallel condition). In addition, patients are poor at reporting the orientation of an individual element but, again, are less prone to interference from nearby distractors, a phenomenon known as visual crowding.

CONCLUSIONS

We suggest that patients' poor performance at contour perception arises not as a consequence of an "integration deficit" but from a combination of reduced sensitivity to local orientation and abnormalities in contextual processing. We propose that this is a consequence of abnormal gain control, a phenomenon that has been implicated in orientation-selectivity as well as surround suppression.