A dynamic nonlinearity and spatial phase specificity in macaque V1 neurons

Dynamic Recruitment of the Feedforward and Recurrent Mechanism for Black-White Asymmetry in the Primary Visual Cortex

Black and white information is asymmetrically distributed in natural scenes, evokes asymmetric neuronal responses, and causes asymmetric perceptions. Recognizing the universality and essentiality of black-white asymmetry in visual information processing, the neural substrates for black-white asymmetry remain unclear. To disentangle the role of the feedforward and recurrent mechanisms in the generation of cortical black-white asymmetry, we recorded the V1 laminar responses and LGN responses of anesthetized cats of both sexes. In a cortical column, we found that black-white asymmetry starts at the input layer and becomes more pronounced in the output layer. We also found distinct dynamics of black-white asymmetry between the output layer and the input layer. Specifically, black responses dominate in all layers after stimulus onset. After stimulus offset, black and white responses are balanced in the input layer, but black responses still dominate in the output layer. Compared with that in the input layer, the rebound response in the output layer is significantly suppressed. The relative suppression strength evoked by white stimuli is notably stronger and depends on the location within the ON-OFF cortical map. A model with delayed and polarity-selective cortical suppression explains black-white asymmetry in the output layer, within which prominent recurrent connections are identified by Granger causality analysis. In addition to black-white asymmetry in response strength, the interlaminar differences in spatial receptive field varied dynamically. Our findings suggest that the feedforward and recurrent mechanisms are dynamically recruited for the generation of black-white asymmetry in V1.SIGNIFICANCE STATEMENT Black-white asymmetry is universal and essential in visual information processing, yet the neural substrates for cortical black-white asymmetry remain unknown. Leveraging V1 laminar recordings, we provided the first laminar pattern of black-white asymmetry in cat V1 and found distinct dynamics of black-white asymmetry between the output layer and the input layer. Comparing black-white asymmetry across three visual hierarchies, the LGN, V1 input layer, and V1 output layer, we demonstrated that the feedforward and recurrent mechanisms are dynamically recruited for the generation of cortical black-white asymmetry. Our findings not only enhance our understanding of laminar processing within a cortical column but also elucidate how feedforward connections and recurrent connections interact to shape neuronal response properties.

Modeling the development of cortical responses in primate dorsal ("where") pathway to optic flow using hierarchical neural field models

Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral ("what") pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal ("where") pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway.

Cortical mechanisms of visual brightness

The primary visual cortex signals the onset of light and dark stimuli with ON and OFF cortical pathways. Here, we demonstrate that both pathways generate similar response increments to large homogeneous surfaces and their response average increases with surface brightness. We show that, in cat visual cortex, response dominance from ON or OFF pathways is bimodally distributed when stimuli are smaller than one receptive field center but unimodally distributed when they are larger. Moreover, whereas small bright stimuli drive opposite responses from ON and OFF pathways (increased versus suppressed activity), large bright surfaces drive similar response increments. We show that this size-brightness relation emerges because strong illumination increases the size of light surfaces in nature and both ON and OFF cortical neurons receive input from ON thalamic pathways. We conclude that visual scenes are perceived as brighter when the average response increments from ON and OFF cortical pathways become stronger.

Mazade et al. find that the visual cortex encodes brightness differently for small than large stimuli. Bright small stimuli drive cortical pathways signaling lights and suppress cortical pathways signaling darks. Conversely, large surfaces drive response increments from both pathways and appear brightest when the response average is strongest.

Different roles of subcortical inputs in V1 responses to luminance and contrast

Cells in the primary visual cortex (V1) generally respond weakly to large uniform luminance stimuli. Only a subset of V1 cells is thought to encode uniform luminance information. In natural scenes, local luminance is an important feature for defining an object that varies and coexists with local spatial contrast. However, the strategies used by V1 cells to encode local mean luminance for spatial contrast stimuli remain largely unclear. Here, using extracellular recordings in anesthetized cats, we investigated the responses of V1 cells by comparing with those of retinal ganglion (RG) cells and lateral geniculate nucleus (LGN) cells to simultaneous and rapid changes in luminance and spatial contrast. Almost all V1 cells exhibited a strong monotonic increasing luminance tuning when they were exposed to high spatial contrast. Thus, V1 cells encode the luminance carried by spatial contrast stimuli with the monotonically increasing response function. Moreover, high contrast decreased luminance tuning of OFF cells but increased that of in ON cells in RG and LGN. The luminance and contrast tunings of LGN ON cells were highly separable as V1 cells, whereas those of LGN OFF cells were lowly separable. These asymmetrical effects of spatial contrast on ON/OFF channels might underlie the robust ability of V1 cells to perform luminance tuning when exposed to spatial contrast stimuli.

A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders

The study of complex systems deals with emergent behavior that arises as a result of nonlinear spatiotemporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behavior and disorders can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science—usually in parallel with traditional methodologies. Here, we illustrate the basic properties that characterize complex systems and evaluate how they relate to what we have learned about brain structure and function from neuroimaging experiments. We then argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from noncomplex system approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.

Cortical Balance Between ON and OFF Visual Responses Is Modulated by the Spatial Properties of the Visual Stimulus

The primary visual cortex of carnivores and primates is dominated by the OFF visual pathway and responds more strongly to dark than light stimuli. Here, we demonstrate that this cortical OFF dominance is modulated by the size and spatial frequency of the stimulus in awake primates and we uncover a main neuronal mechanism underlying this modulation. We show that large grating patterns with low spatial frequencies drive five times more OFF-dominated than ON-dominated neurons, but this pronounced cortical OFF dominance is strongly reduced when the grating size decreases and the spatial frequency increases, as when the stimulus moves away from the observer. We demonstrate that the reduction in cortical OFF dominance is not caused by a selective reduction of visual responses in OFF-dominated neurons but by a change in the ON/OFF response balance of neurons with diverse receptive field properties that can be ON or OFF dominated, simple, or complex. We conclude that cortical OFF dominance is continuously adjusted by a neuronal mechanism that modulates ON/OFF response balance in multiple cortical neurons when the spatial properties of the visual stimulus change with viewing distance and/or optical blur.

Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

A central transformation that occurs within mammalian visual cortex is the change from linear, polarity-sensitive responses to nonlinear, polarity-insensitive responses. These neurons are classically labelled as either simple or complex, respectively, on the basis of their response linearity (Skottun et al., 1991). While the difference between cell classes is clear when the stimulus strength is high, reducing stimulus strength diminishes the differences between the cell types and causes some complex cells to respond as simple cells (Crowder et al., 2007; van Kleef et al., 2010; Hietanen et al., 2013). To understand the synaptic basis for this shift in behavior, we used in vivo whole-cell recordings while systematically shifting stimulus contrast. We find systematic shifts in the degree of complex cell responses in mouse primary visual cortex (V1) at the subthreshold level, demonstrating that synaptic inputs change in concert with the shifts in response linearity and that the change in response linearity is not simply due to the threshold nonlinearity. These shifts are consistent with a visual cortex model in which the recurrent amplification acts as a critical component in the generation of complex cell responses (Chance et al., 1999).

Maintained avalanche dynamics during task-induced changes of neuronal activity in nonhuman primates

Sensory events, cognitive processing and motor actions correlate with transient changes in neuronal activity. In cortex, these transients form widespread spatiotemporal patterns with largely unknown statistical regularities. Here, we show that activity associated with behavioral events carry the signature of scale-invariant spatiotemporal clusters, neuronal avalanches. Using high-density microelectrode arrays in nonhuman primates, we recorded extracellular unit activity and the local field potential (LFP) in premotor and prefrontal cortex during motor and cognitive tasks. Unit activity and negative LFP deflections (nLFP) consistently changed in rate at single electrodes during tasks. Accordingly, nLFP clusters on the array deviated from scale-invariance compared to ongoing activity. Scale-invariance was recovered using ‘adaptive binning’, that is identifying clusters at temporal resolution given by task-induced changes in nLFP rate. Measures of LFP synchronization confirmed and computer simulations detailed our findings. We suggest optimization principles identified for avalanches during ongoing activity to apply to cortical information processing during behavior.

V1 neurons respond to luminance changes faster than contrast changes

Luminance and contrast are two major attributes of objects in the visual scene. Luminance and contrast information received by visual neurons are often updated simultaneously. We examined the temporal response properties of neurons in the primary visual cortex (V1) to stimuli whose luminance and contrast were simultaneously changed by 50 Hz. We found that response tuning to luminance changes precedes tuning to contrast changes in V1. For most V1 neurons, the onset time of response tuning to luminance changes was shorter than that to contrast changes. Most neurons carried luminance information in the early response stage, while all neurons carried both contrast and luminance information in the late response stage. The early luminance response suggests that cortical processing for luminance is not as slow as previously thought.

A texture-processing model of the 'visual sense of number'

It has been suggested that numerosity is an elementary quality of perception, similar to colour. If so (and despite considerable investigation), its mechanism remains unknown. Here, we show that observers require on average a massive difference of approximately 40% to detect a change in the number of objects that vary irrelevantly in blur, contrast and spatial separation, and that some naive observers require even more than this. We suggest that relative numerosity is a type of texture discrimination and that a simple model computing the contrast energy at fine spatial scales in the image can perform at least as well as human observers. Like some human observers, this mechanism finds it harder to discriminate relative numerosity in two patterns with different degrees of blur, but it still outpaces the human. We propose energy discrimination as a benchmark model against which more complex models and new data can be tested.

Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434-444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155-1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457-3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.

From Exploration to Fixation: An Integrative View of Yarbus's Vision

Alfred Lukyanovich Yarbus (1914-1986) pioneered the study of stabilized retinal images, miniature eye movements, and the cognitive influences that act on visual scanning. Yarbus's studies of these different topics have remained fundamentally disconnected and independent of each other, however. In this review, we propose that Yarbus's various research lines are instead deeply and intrinsically interconnected, as are the small eye movements produced during visual fixation and the large-scale scanning patterns associated with visual exploration of objects and scenes. Such apparently disparate viewing behaviors may represent the extremes of a single continuum of oculomotor performance that operates across spatial scales when we search the visual world.

A computational theory of visual receptive fields

A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world. These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space-time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales. It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations. There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space-time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space-time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli. This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.

Stability of simple/complex classification with contrast and extraclassical receptive field modulation in macaque V1

A key property of neurons in primary visual cortex (V1) is the distinction between simple and complex cells. Recent reports in cat visual cortex indicate the categorization of simple and complex can change depending on stimulus conditions. We investigated the stability of the simple/complex classification with changes in drive produced by either contrast or modulation by the extraclassical receptive field (eCRF). These two conditions were reported to increase the proportion of simple cells in cat cortex. The ratio of the modulation depth of the response (F1) to the elevation of response (F0) to a drifting grating (F1/F0 ratio) was used as the measure of simple/complex. The majority of V1 complex cells remained classified as complex with decreasing contrast. Near contrast threshold, an equal proportion of simple and complex cells changed their classification. The F1/F0 ratio was stable between optimal and large stimulus areas even for those neurons that showed strong eCRF suppression. There was no discernible overall effect of surrounding spatial context on the F1/F0 ratio. Simple/complex cell classification is relatively stable across a range of stimulus drives, produced by either contrast or eCRF suppression.

Effects of contrast and contrast adaptation on static receptive field features in macaque area V1

The spatiotemporal features of the “static” receptive field (RF), as revealed with flashing bars or spots, determine other RF properties. We examined how some of these static RF features vary with contrast and contrast adaptation in area V1 of the anesthetized macaque monkey. RFs were mapped with light and dark flashing bars presented at three different contrasts, with the low and medium contrasts eliciting approximately 1/3 and 2/3 of the high-contrast response amplitude. The main results are as follows: 1) RF widths decreased when contrast decreased; however, the amount of decrease was less than that expected from an iceberg model and closer to the expectation of a contrast invariance of the RF width. 2) Area tuning experiments with drifting gratings showed an opposite effect of contrast: an increase in preferred stimulus diameter when contrast decreased. This implies that the effect of contrast on preferred stimulus size is not predictable from the static RF. 3) Contrast adaptation attenuated the effect of contrast on RF amplitude but did not significantly modify RF width. 4) RF subregion overlap was only marginally affected by changes in contrast and contrast adaptation; the classification of cells as simple and complex, when established from subregion overlap, appears to be robust with respect to changes in contrast and adaptation state. Previous studies have shown that the spatiotemporal features of the RF depend largely on the stimuli used to map the RF. This study shows that contrast is one elemental feature that contributes to the dynamics of the RF.

Standardized F1: a consistent measure of strength of modulation of visual responses to sine-wave drifting gratings

The magnitude of spike-responses of neurons in the mammalian visual system to sine-wave luminance-contrast-modulated drifting gratings is modulated by the temporal frequency of the stimulation. However, there are serious problems with consistency and reliability of the traditionally used methods of assessment of strength of such modulation. Here we propose an intuitive and simple tool for assessment of the strength of modulations in the form of standardized F1 index, zF1. We define zF1 as the ratio of the difference between the F1 (component of amplitude spectrum of the spike-response at temporal frequency of stimulation) and the mean value of spectrum amplitudes to standard deviation along all frequencies in the spectrum. In order to assess the validity of this measure, we have: (1) examined behavior of zF1 using spike-responses to optimized drifting gratings of single neurons recorded from four 'visual' structures (area V1 of primary visual cortex, superior colliculus, suprageniculate nucleus and caudate nucleus) in the brain of commonly used visual mammal - domestic cat; (2) compared the behavior of zF1 with that of classical statistics commonly employed in the analysis of steady-state responses; (3) tested the zF1 index on simulated spike-trains generated with threshold-linear model. Our analyses indicate that zF1 is resistant to distortions due to the low spike count in responses and therefore can be particularly useful in the case of recordings from neurons with low firing rates and/or low net mean responses. While most V1 and a half of caudate neurons exhibit high zF1 indices, the majorities of collicular and suprageniculate neurons exhibit low zF1 indices. We conclude that despite the general shortcomings of measuring strength of modulation inherent in the linear system approach, zF1 can serve as a sensitive and easy to interpret tool for detection of modulation and assessment of its strength in responses of visual neurons.

Laminar analysis of visually evoked activity in the primary visual cortex

Studying the laminar pattern of neural activity is crucial for understanding the processing of neural signals in the cerebral cortex. We measured neural population activity [multiunit spike activity (MUA) and local field potential, LFP] in Macaque primary visual cortex (V1) in response to drifting grating stimuli. Sustained visually driven MUA was at an approximately constant level across cortical depth in V1. However, sustained, visually driven, local field potential power, which was concentrated in the γ-band (20-60 Hz), was greatest at the cortical depth corresponding to cortico-cortical output layers 2, 3, and 4B. γ-band power also tends to be more sustained in the output layers. Overall, cortico-cortical output layers accounted for 67% of total γ-band activity in V1, whereas 56% of total spikes evoked by drifting gratings were from layers 2, 3, and 4B. The high-resolution layer specificity of γ-band power, the laminar distribution of MUA and γ-band activity, and their dynamics imply that neural activity in V1 is generated by laminar-specific mechanisms. In particular, visual responses of MUA and γ-band activity in cortico-cortical output layers 2, 3, and 4B seem to be strongly influenced by laminar-specific recurrent circuitry and/or feedback.

Local sensitivity to stimulus orientation and spatial frequency within the receptive fields of neurons in visual area 2 of macaque monkeys

We used dynamic dense noise stimuli and local spectral reverse correlation methods to reveal the local sensitivities of neurons in visual area 2 (V2) of macaque monkeys to orientation and spatial frequency within their receptive fields. This minimized the potentially confounding assumptions that are inherent in stimulus selections. The majority of neurons exhibited a relatively high degree of homogeneity for the preferred orientations and spatial frequencies in the spatial matrix of facilitatory subfields. However, about 20% of all neurons showed maximum orientation differences between neighboring subfields that were greater than 25 deg. The neurons preferring horizontal or vertical orientations showed less inhomogeneity in space than the neurons preferring oblique orientations. Over 50% of all units also exhibited suppressive profiles, and those were more heterogeneous than facilitatory profiles. The preferred orientation and spatial frequency of suppressive profiles differed substantially from those of facilitatory profiles, and the neurons with suppressive subfields had greater orientation selectivity than those without suppressive subfields. The peak suppression occurred with longer delays than the peak facilitation. These results suggest that the receptive field profiles of the majority of V2 neurons reflect the orderly convergence of V1 inputs over space, but that a subset of V2 neurons exhibit more complex response profiles having both suppressive and facilitatory subfields. These V2 neurons with heterogeneous subfield profiles could play an important role in the initial processing of complex stimulus features.

Rapid dynamics of contrast responses in the cat primary visual cortex

The visual information we receive during natural vision changes rapidly and continuously. The visual system must adapt to the spatiotemporal contents of the environment in order to efficiently process the dynamic signals. However, neuronal responses to luminance contrast are usually measured using drifting or stationary gratings presented for a prolonged duration. Since motion in our visual field is continuous, the signals received by the visual system contain an abundance of transient components in the contrast domain. Here using a modified reverse correlation method, we studied the properties of responses of neurons in the cat primary visual cortex to different contrasts of grating stimuli presented statically and transiently for 40 ms, and showed that neurons can effectively discriminate the rapidly changing contrasts. The change in the contrast response function (CRF) over time mainly consisted of an increment in contrast gain (CRF shifts to left) in the developing phase of temporal responses and a decrement in response gain (CRF shifts downward) in the decay phase. When the distribution range of stimulus contrasts was increased, neurons demonstrated decrement in contrast gain and response gain. Our results suggest that contrast gain control (contrast adaptation) and response gain control mechanisms are well established during the first tens of milliseconds after stimulus onset and may cooperatively mediate the rapid dynamic responses of visual cortical neurons to the continuously changing contrast. This fast contrast adaptation may play a role in detecting contrast contours in the context of visual scenes that are varying rapidly.

Features and the 'primal sketch'

This review is concerned primarily with psychophysical and physiological evidence relevant to the question of the existence of spatial features or spatial primitives in human vision. The review will be almost exclusively confined to features defined in the luminance domain. The emphasis will be on the experimental and computational methods that have been used for revealing features, rather than on a detailed comparison between different models of feature extraction. Color and texture fall largely outside the scope of the review, though the principles may be similar. Stereo matching and motion matching are also largely excluded because they are covered in other contributions to this volume, although both have addressed the question of the spatial primitives involved in matching. Similarities between different psychophysically-based model will be emphasized rather than minor differences. All the models considered in the review are based on the extraction of directional spatial derivatives of the luminance profile, typically the first and second, but in one case the third order, and all have some form of non-linearity, be it rectification or thresholding.

Microsaccades: a neurophysiological analysis

Microsaccades are the largest and fastest of the fixational eye movements, which are involuntary eye movements produced during attempted visual fixation. In recent years, the interaction between microsaccades, perception and cognition has become one of the most rapidly growing areas of study in visual neuroscience. The neurophysiological consequences of microsaccades have been the focus of less attention, however, as have the oculomotor mechanisms that generate and control microsaccades. Here we review the latest neurophysiological findings concerning microsaccades and discuss their relationships to perception and cognition. We also point out the current gaps in our understanding of the neurobiology of microsaccades and identify the most promising lines of enquiry.

The linearity and selectivity of neuronal responses in awake visual cortex

Neurons in primary visual cortex (V1) are frequently classified based on their response linearity: the extent to which their visual responses to drifting gratings resemble a linear replica of the stimulus. This classification is supported by the finding that response linearity is bimodally distributed across neurons in area V1 of anesthetized animals. However, recent studies suggest that such bimodal distribution may not reflect two neuronal types but a nonlinear relationship between the membrane potential and the spike output. A main limitation of these previous studies is that they measured response linearity in anesthetized animals, where the distance between the neuronal membrane potential and the spike threshold is artificially increased by anesthesia. Here, we measured V1 response linearity in the awake brain and its correlation with the neuronal spontaneous firing rate, which is related to the distance between membrane potential and threshold. Our results demonstrate that response linearity is bimodally distributed in awake V1 but that it is poorly correlated with spontaneous firing rate. In contrast, the spontaneous firing rate is best correlated to the response selectivity and response latency to stimuli.

Stimulus ensemble and cortical layer determine V1 spatial receptive fields

The concept of receptive field is a linear, feed-forward view of visual signal processing. Frequently used models of V1 neurons, like the dynamic Linear filter--static nonlinearity--Poisson [corrected] spike encoder model, predict that receptive fields measured with different stimulus ensembles should be similar. Here, we tested this concept by comparing spatiotemporal maps of V1 neurons derived from two very different, but commonly used, stimulus ensembles: sparse noise and Hartley subspace stimuli. We found maps from the two methods agreed for neurons in input layer 4C but were very different for neurons in superficial layers of V1. Many layer 2/3 cells have receptive fields with multiple elongated subregions when mapped with Hartley stimuli, but their spatial maps collapse to only a single, less-elongated subregion when mapped with sparse noise. Moreover, for upper layer V1 neurons, the preferred orientation for Hartley maps is much closer to the preferred orientation measured with drifting gratings than is the orientation preference of sparse-noise maps. These results challenge the concept of a stimulus-invariant receptive field and imply that intracortical interactions shape fundamental properties of layer 2/3 neurons.

Predicting perception of the wagon wheel illusion

Stroboscopic illumination of a rapidly rotating disk with radial spokes leads to a range of different stationary and moving images as the angular rotation frequency of the disk and the strobe frequency are varied. We compare predictions from the standard correlation model of motion perception with a model based on phase locking observed during periodic stimulation of an integrate-and-fire nonlinear oscillator. The close agreement between theoretical predictions and experimental observations suggests the possibility that periodic forcing of nonlinear neural oscillations may play a role in motion perception.

Linear and nonlinear systems analysis of the visual system: why does it seem so linear? A review dedicated to the memory of Henk Spekreijse

Linear and nonlinear systems analysis are tools that can be used to study communication systems like the visual system. The first step of systems analysis often is to test whether or not the system is linear. Retinal pathways are surprisingly linear, and some neurons in the visual cortex also emulate linear sensory transducers. We conclude that the retinal linearity depends on specialized ribbon synapses while cortical linearity is the result of balanced excitatory and inhibitory synaptic interactions.

Dynamics of tuning in the Fourier domain

Neurons in primary visual cortex (area V1) are jointly tuned to the orientation and spatial frequency of sinusoidal stimuli (the Fourier domain). The role that suppressive mechanisms play in shaping the tuning and dynamics of cortical responses remains the subject of debate. Here we used subspace reverse correlation to study the relationship between suppression by nonoptimal stimuli, the spectral-temporal separability of the responses, and their persistence in time. Two clear relationships emerged from our data. First, cells with inseparable responses were often accompanied by suppression to nonpreferred stimuli, while separable responses showed mostly enhancement by their preferred stimuli. Second, inseparable responses were characterized by a longer persistence in time compared with those with separable dynamics. A parametric model that assumes the additive combination of separable enhancement and suppression signals, with suppression constrained to be low-pass in spatial frequency and untuned for orientation, explained the data well. These new findings, in addition to an established correlation between selectivity and suppression for nonoptimal stimuli, clarify how the dynamics and selectivity of cortical responses are shaped by suppressive signals and how their interplay generates the large diversity of responses observed in primary visual cortex.