Complex Cells Increase Their Phase Sensitivity at Low Contrasts and Following Adaptation

Periodic clustering of simple and complex cells in visual cortex

Neurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons or if they represent a continuum of variation within a single class of cells. Herein, we show that simple and complex cells may arise commonly from the feedforward projections from the retina. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. From data in cats, we observed that clusters of simple and complex receptive fields correlate topographically with orientation maps, which supports our model prediction. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.

Contrast adaptation and interocular transfer in cortical cells: A re-analysis & a two-stage gain-control model of binocular combination

How do V1 cells respond to, adapt to, and combine signals from the two eyes? We tested a simple functional model that has monocular and binocular stages of divisive contrast gain control (CGC) that sit before, and after, binocular summation respectively. Interocular suppression (IOS) was another potential influence on contrast gain. Howarth, Vorobyov & Sengpiel (2009, Cerebral Cortex, 19, 1835-1843) studied contrast adaptation and interocular transfer in cat V1 cells. In our re-analysis we found that ocular dominance (OD) and contrast adaptation at a fixed test contrast were well described by a re-scaling of the unadapted orientation tuning curve - a simple change in response gain. We compared six variants of the basic model, and one model fitted the gain data notably better than the others did. When the dominant eye was tested, adaptation reduced cell response gain more when that eye was adapted than when the other eye was adapted. But when the non-dominant eye was tested, adapting either eye gave about the same reduction in overall gain, and there was an interaction between OD and adapting eye that was well described by the best-fitting model. Two key features of this model are that signals driving IOS arise 'early', before attenuation due to OD, while suppressive CGC signals are 'late' and so affected by OD. We show that late CGC confers a functional advantage: it yields partial compensation for OD, which should reduce ocular imbalance at the input to binocular summation, and improve the cell's sensitivity to variation in stereo disparity.

Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

Visual object identification requires both selectivity for specific visual features that are important to the object's identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognized as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we quantitatively describe the balance between selectivity and invariance in complex cells. Phase invariance is frequently partial, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

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.

Comparison of contrast-dependent phase sensitivity in primary visual cortex of mouse, cat and macaque

Neurones in the primary visual cortex (V1) are classified into simple and complex types. Simple cells are phase-sensitive, that is, they modulate their responses according to the position and brightness polarity of edges in their receptive fields. Complex cells are phase invariant, that is, they respond to edges in their receptive fields regardless of location or brightness polarity. Simple and complex cells are quantified by the degree of sensitivity to the spatial phases of drifting sinusoidal gratings. Some V1 complex cells become more phase-sensitive at low contrasts. Here we use a standardized analysis method for data derived from grating stimuli developed for macaques to reanalyse data previously collected from cats, and also collect and analyse the responses of 73 mouse V1 neurons. The analysis provides the first consistent comparative study of contrast-dependent phase sensitivity in V1 of mouse, cat and macaque monkey.

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).

Contrast-dependent phase sensitivity in area MT of macaque visual cortex

In primate visual cortex (V1), about half the neurons are sensitive to the spatial phases of grating stimuli and generate highly modulated responses to drifting gratings (simple cells). The remaining cells show far less phase sensitivity and relatively unmodulated responses to moving gratings (complex cells). In the second visual area (V2) and the motion processing area MT (or V5), the majority of cells have unmodulated responses to drifting gratings - they are phase invariant. At just-detectable contrasts, 44% of V1 complex cells show highly modulated responses, but this contrast-dependent phase sensitivity is found in only 7% of V2 complex cells. We recorded from 149 cells in macaque MT - 142 classed as complex cells at high contrast. Approximately 14% (20/142) of MT complex cells showed significantly modulated responses to drifting gratings at just-detectable contrasts. A general feature of MT cells is that they can be divided into pattern and component selective types, but we found no correlation between this classification and contrast-dependent phase sensitivity. Phase sensitivity in MT is discussed in relation to MT's input structure.

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.

Neurons in primary visual cortex represent distribution of luminance

To efficiently detect a wide range of light-intensity changes, visual neurons must adapt to ambient luminance. However, how neurons in the primary visual cortex (V1) code the distribution of luminance remains unknown. We designed stimuli that represent rapid changes in luminance under different luminance distributions and investigated V1 neuron responses to these novel stimuli. We demonstrate that V1 neurons represent luminance changes by dynamically adjusting their responses when the luminance distribution changes. Many cells (35%) detected luminance changes by responding to dark stimuli when the distribution was dominated by bright stimuli, bright stimuli when dominated by dark stimuli, and both dark and bright stimuli when dominated by intermediate luminance stimuli; 13% of cells signaled the mean luminance that was varied with different distributions; the remaining 52% of cells gradually shifted the responses that were most sensitive to luminance changes when the luminance distribution varied. The remarkable response changes of the former two cell groups suggest their crucial roles in detecting luminance changes. These response characteristics demonstrate that V1 neurons are not only sensitive to luminance change, but also luminance distribution change. They encode luminance changes according to the luminance distribution. Mean cells represent the prevailing luminance and reversal cells represent the salient stimuli in the environment.

Neural coding of image structure and contrast polarity of Cartesian, hyperbolic, and polar gratings in the primary and secondary visual cortex of the tree shrew

We comprehensively characterize spiking and visual evoked potential (VEP) activity in tree shrew V1 and V2 using Cartesian, hyperbolic, and polar gratings. Neural selectivity to structure of Cartesian gratings was higher than other grating classes in both visual areas. From V1 to V2, structure selectivity of spiking activity increased, whereas corresponding VEP values tended to decrease, suggesting that single-neuron coding of Cartesian grating attributes improved while the cortical columnar organization of these neurons became less precise from V1 to V2. We observed that neurons in V2 generally exhibited similar selectivity for polar and Cartesian gratings, suggesting that structure of polar-like stimuli might be encoded as early as in V2. This hypothesis is supported by the preference shift from V1 to V2 toward polar gratings of higher spatial frequency, consistent with the notion that V2 neurons encode visual scene borders and contours. Neural sensitivity to modulations of polarity of hyperbolic gratings was highest among all grating classes and closely related to the visual receptive field (RF) organization of ON- and OFF-dominated subregions. We show that spatial RF reconstructions depend strongly on grating class, suggesting that intracortical contributions to RF structure are strongest for Cartesian and polar gratings. Hyperbolic gratings tend to recruit least cortical elaboration such that the RF maps are similar to those generated by sparse noise, which most closely approximate feedforward inputs. Our findings complement previous literature in primates, rodents, and carnivores and highlight novel aspects of shape representation and coding occurring in mammalian early visual cortex.

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.

Representation of higher-order statistical structures in natural scenes via spatial phase distributions

Natural scenes contain richer perceptual information in their spatial phase structure than their amplitudes. Modeling phase structure of natural scenes may explain higher-order structure inherent to the natural scenes, which is neglected in most classical models of redundancy reduction. Only recently, a few models have represented images using a complex form of receptive fields (RFs) and analyze their complex responses in terms of amplitude and phase. However, these complex representation models often tacitly assume a uniform phase distribution without empirical support. The structure of spatial phase distributions of natural scenes in the form of relative contributions of paired responses of RFs in quadrature has not been explored statistically until now. Here, we investigate the spatial phase structure of natural scenes using complex forms of various Gabor-like RFs. To analyze distributions of the spatial phase responses, we constructed a mixture model that accounts for multi-modal circular distributions, and the EM algorithm for estimation of the model parameters. Based on the likelihood, we report presence of both uniform and structured bimodal phase distributions in natural scenes. The latter bimodal distributions were symmetric with two peaks separated by about 180°. Thus, the redundancy in the natural scenes can be further removed by using the bimodal phase distributions obtained from these RFs in the complex representation models. These results predict that both phase invariant and phase sensitive complex cells are required to represent the regularities of natural scenes in visual systems.

Contrast-dependent phase sensitivity in V1 but not V2 of macaque visual cortex

Some neurons in early visual cortex are highly selective for the position of oriented edges in their receptive fields (simple cells), whereas others are largely position insensitive (complex cells). These characteristics are reflected in their sensitivity to the spatial phase of moving sine-wave gratings: simple cell responses oscillate at the fundamental frequency of the stimulus, whereas this is less so for complex cells. In primates, when assessed at high stimulus contrast, simple cells and complex cells are roughly equally represented in the first visual cortical area, V1, whereas in the second visual area, V2, the majority of cells are complex. Recent evidence has shown that phase sensitivity of complex cells is contrast dependent. This has led to speculation that reduced contrast may lead to changes in the spatial structure of receptive fields, perhaps due to changes in how feedforward and recurrent signals interact. Given the substantial interconnections between V1 and V2 and recent evidence for the emergence of unique functional capacity in V2, we assess the relationship between contrast and phase sensitivity in the two brain regions. We show that a substantial proportion of complex cells in macaque V1 exhibit significant increases in phase sensitivity at low contrast, whereas this is rarely observed in V2. Our results support a degree of hierarchical processing from V1 to V2 with the differences possibly relating to the fact that V1 combines both subcortical and cortical input, whereas V2 receives input purely from cortical circuits.

A simpler primate brain: the visual system of the marmoset monkey

Humans are diurnal primates with high visual acuity at the center of gaze. Although primates share many similarities in the organization of their visual centers with other mammals, and even other species of vertebrates, their visual pathways also show unique features, particularly with respect to the organization of the cerebral cortex. Therefore, in order to understand some aspects of human visual function, we need to study non-human primate brains. Which species is the most appropriate model? Macaque monkeys, the most widely used non-human primates, are not an optimal choice in many practical respects. For example, much of the macaque cerebral cortex is buried within sulci, and is therefore inaccessible to many imaging techniques, and the postnatal development and lifespan of macaques are prohibitively long for many studies of brain maturation, plasticity, and aging. In these and several other respects the marmoset, a small New World monkey, represents a more appropriate choice. Here we review the visual pathways of the marmoset, highlighting recent work that brings these advantages into focus, and identify where additional work needs to be done to link marmoset brain organization to that of macaques and humans. We will argue that the marmoset monkey provides a good subject for studies of a complex visual system, which will likely allow an important bridge linking experiments in animal models to humans.

The spatial summation characteristics of three categories of V1 neurons differing in non-classical receptive field modulation properties

The spatial summation of excitation and inhibition determines the final output of neurons in the cat V1. To characterize the spatial extent of the excitatory classical receptive field (CRF) and inhibitory non-classical receptive field (nCRF) areas, we examined the spatial summation properties of 169 neurons in cat V1 at high (20-90%) and low (5-15%) stimulus contrasts. Three categories were classified based on the difference in the contrast dependency of the surround suppression. We discovered that the three categories significantly differed in CRF size, peak firing rate, and the proportion of simple/complex cell number. The classification of simple and complex cells was determined at both high and low contrasts. While the majority of V1 neurons had stable modulation ratios in their responses, 10 cells (6.2%) in our sample crossed the classification boundary under different stimulus contrasts. No significant difference was found in the size of the CRF between simple and complex cells. Further comparisons in each category determined that the CRFs for complex cells were significantly larger than those for simple cells in category type I neurons, with no significant differences between simple and complex cells in category type II and type III neurons. In addition, complex cells have higher peak firing rates than simple cells.

Neural mechanism for sensing fast motion in dim light

Luminance is a fundamental property of visual scenes. A population of neurons in primary visual cortex (V1) is sensitive to uniform luminance. In natural vision, however, the retinal image often changes rapidly. Consequently the luminance signals visual cells receive are transiently varying. How V1 neurons respond to such luminance changes is unknown. By applying large static uniform stimuli or grating stimuli altering at 25 Hz that resemble the rapid luminance changes in the environment, we show that approximately 40% V1 cells responded to rapid luminance changes of uniform stimuli. Most of them strongly preferred luminance decrements. Importantly, when tested with drifting gratings, the preferred speeds of these cells were significantly higher than cells responsive to static grating stimuli but not to uniform stimuli. This responsiveness can be accounted for by the preferences for low spatial frequencies and high temporal frequencies. These luminance-sensitive cells subserve the detection of fast motion under the conditions of dim illumination.

Spatiotemporal specificity of contrast adaptation in mouse primary visual cortex

Prolonged viewing of high contrast gratings alters perceived stimulus contrast, and produces characteristic changes in the contrast response functions of neurons in the primary visual cortex (V1). This is referred to as contrast adaptation. Although contrast adaptation has been well-studied, its underlying neural mechanisms are not well-understood. Therefore, we investigated contrast adaptation in mouse V1 with the goal of establishing a quantitative description of this phenomenon in a genetically manipulable animal model. One interesting aspect of contrast adaptation that has been observed both perceptually and in single unit studies is its specificity for the spatial and temporal characteristics of the stimulus. Therefore, in the present work we determined if the magnitude of contrast adaptation in mouse V1 neurons was dependent on the spatial frequency and temporal frequency of the adapting grating. We used protocols that were readily comparable with previous studies in cats and primates, and also a novel contrast ramp stimulus that characterized the spatial and temporal specificity of contrast adaptation simultaneously. Similar to previous work in higher mammals, we found that contrast adaptation was strongest when the spatial frequency and temporal frequency of the adapting grating matched the test stimulus. This suggests similar mechanisms underlying contrast adaptation across animal models and indicates that the rapidly advancing genetic tools available in mice could be used to provide insights into this phenomenon.

On the relation between receptive field structure and stimulus selectivity in the tree shrew primary visual cortex

There are notable differences in functional properties of primary visual cortex (V1) neurons among mammalian species, particularly those concerning the occurrence of simple and complex cells and the generation of orientation selectivity. Here, we present quantitative data on receptive field (RF) structure, response modulation, and orientation tuning for single neurons in V1 of the tree shrew, a close relative of primates. We find that spatial RF subfield segregation, a criterion for identifying simple cells, was exceedingly small in the tree shrew V1. In contrast, many neurons exhibited elevated F1/F0 modulation that is often used as a simple cell marker. This apparent discrepancy can be explained by the robust stimulus polarity preference in tree shrew V1, which inflates F1/F0 ratio values. RF structure mapped with sparse-noise-which is spatially restricted and emphasizes thalamo-cortical feed-forward inputs-appeared unrelated to orientation selectivity. However, RF structure mapped using the Hartley subspace stimulus-which covers a large area of the visual field and recruits considerable intracortical processing-did predict orientation preference. Our findings reveal a number of striking similarities in V1 functional organization between tree shrews and primates, emphasizing the important role of intracortical recurrent processing in shaping V1 response properties in these species.

Phase sensitivity of complex cells in primary visual cortex

Neurons in the primary visual cortex are often classified as either simple or complex based on the linearity (or otherwise) of their response to spatial luminance contrast. In practice, classification is typically based on Fourier analysis of a cell's response to an optimal drifting sine-wave grating. Simple cells are generally considered to be linear and produce responses modulated at the fundamental frequency of the stimulus grating. In contrast, complex cells exhibit significant nonlinearities that reduce the response at the fundamental frequency. Cells can therefore be easily and objectively classified based on the relative modulation of their responses - the ratio of the phase-sensitive response at the fundamental frequency of the stimulus (F₁) to the phase-invariant sustained response (F₀). Cells are classified as simple if F₁/F₀>1 and complex if F₁/F₀<1. This classification is broadly consistent with criteria based on the spatial organisation of cells' receptive fields and is accordingly presumed to reflect disparate functional roles of simple and complex cells in coding visual information. However, Fourier analysis of spiking responses is sensitive to the number of spikes available - F₁/F₀ increases as the number of spikes is reduced, even for phase-invariant complex cells. Moreover, many complex cells encountered in the laboratory exhibit some phase sensitivity, evident as modulation of their responses at the fundamental frequency. There currently exists no objective quantitative means of assessing the significance or otherwise of these modulations. Here we derive a statistical basis for objectively assessing whether the modulation of neuronal responses is reliable, thereby adding a level of statistical certainty to measures of phase sensitivity. We apply our statistical analysis to neuronal responses to moving sine-wave gratings recorded from 367 cells in cat primary visual cortex. We find that approximately 60% of complex cells exhibit statistically significant (α<0.01) modulation of their responses to optimal moving gratings. These complex cells are phase sensitive and reliably encode spatial phase.

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.

Orientation specificity of contrast adaptation in mouse primary visual cortex

Contrast adaptation is a commonly studied phenomenon in vision, where prolonged exposure to spatial contrast alters perceived stimulus contrast and produces characteristic shifts in the contrast response functions of primary visual cortex neurons in cats and primates. In this study we investigated contrast adaptation in mouse primary visual cortex with two goals in mind. First, we sought to establish a quantitative description of contrast adaptation in an animal model, where genetic tools are more readily applicable to this phenomenon. Second, the orientation specificity of contrast adaptation was studied to comparatively assess the possible role of local cortical networks in contrast adaptation. In cats and primates, predictable differences in visual processing across the cortical surface are thought to be caused by inhomogeneous local network membership that arises from the pinwheel organization of orientation columns. Because mice lack this pinwheel organization, we predicted that local cortical networks would have access to a broad spectrum of orientation signals, and contrast adaptation in mice would not be specific to the recorded cell's preferred orientation. We found that most mouse V1 neurons showed contrast adaptation that was robust regardless of whether the adapting stimulus matched the cell's preferred orientation or was orthogonal to it.

Development of maps of simple and complex cells in the primary visual cortex

Hubel and Wiesel (1962) classified primary visual cortex (V1) neurons as either simple, with responses modulated by the spatial phase of a sine grating, or complex, i.e., largely phase invariant. Much progress has been made in understanding how simple-cells develop, and there are now detailed computational models establishing how they can form topographic maps ordered by orientation preference. There are also models of how complex cells can develop using outputs from simple cells with different phase preferences, but no model of how a topographic orientation map of complex cells could be formed based on the actual connectivity patterns found in V1. Addressing this question is important, because the majority of existing developmental models of simple-cell maps group neurons selective to similar spatial phases together, which is contrary to experimental evidence, and makes it difficult to construct complex cells. Overcoming this limitation is not trivial, because mechanisms responsible for map development drive receptive fields (RF) of nearby neurons to be highly correlated, while co-oriented RFs of opposite phases are anti-correlated. In this work, we model V1 as two topographically organized sheets representing cortical layer 4 and 2/3. Only layer 4 receives direct thalamic input. Both sheets are connected with narrow feed-forward and feedback connectivity. Only layer 2/3 contains strong long-range lateral connectivity, in line with current anatomical findings. Initially all weights in the model are random, and each is modified via a Hebbian learning rule. The model develops smooth, matching, orientation preference maps in both sheets. Layer 4 units become simple cells, with phase preference arranged randomly, while those in layer 2/3 are primarily complex cells. To our knowledge this model is the first explaining how simple cells can develop with random phase preference, and how maps of complex cells can develop, using only realistic patterns of connectivity.

Complex cell receptive fields: evidence for a hierarchical mechanism

Simple cells in the primary visual cortex have segregated ON and OFF subregions in their receptive fields, while complex cells have overlapping ON and OFF subregions. These two cell types form the extremes at each end of a continuum of receptive field types. Hubel and Wiesel in 1962 suggested a hierarchical scheme of processing whereby spatially offset simple cells drive complex cells. Simple and complex cells are often classified by their responses to moving sine wave gratings: simple cells have oscillatory responses while complex cells produce unmodulated responses. Here, using moving gratings as stimuli, we show that a significant number of cells that display low levels of response modulation at high contrasts demonstrate high levels of response modulation at low contrasts. Most often a drifting low contrast grating generates a large phasic response at the fundamental frequency of the grating (F(1)) and a smaller but significant phasic response that is approximately 180 deg out-of-phase with the F(1) component. We present several models capable of capturing the effects of stimulus contrast on complex cell responses. The model that best reproduces our experimental results is a variation of the classical hierarchical model. In our model several spatially offset simple cells provide input to a complex cell, with each simple cell exhibiting a different contrast response function. At low contrasts only one of these simple cells is sufficiently excited to reveal its receptive field properties. As contrast is increased additional spatially offset simple cells with higher contrast thresholds add their responses to the overall spiking activity.

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.

Dynamic contrast change produces rapid gain control in visual cortex

During normal vision, objects moving in the environment, our own body movements and our eye movements ensure that the receptive fields of visual neurons are being presented with continually changing contrasts. Thus, the visual input during normal behaviour differs from the type of stimuli traditionally used to study contrast coding, which are presented in a step-like manner with abrupt changes in contrast followed by prolonged exposure to a constant stimulus. The abrupt changes in contrast typically elicit brief periods of intense firing with low variability called onset transients. Onset transients provide the visual system with a powerful and reliable cue that the visual input has changed. In this paper we investigate visual processing in the primary visual cortex of cats in response to stimuli that change contrast dynamically. We show that 1-4 s presentations of dynamic increases and decreases in contrast can generate stronger contrast gain control than several minutes exposure to a stimulus of constant contrast. Thus, transient mechanisms of contrast coding are not only less variable than sustained responses but are also more rapid and flexible. Finally, we propose a quantitative model of contrast coding which accounts for changes in spike rate over time in response to dynamically changing image contrast.

Influence of adapting speed on speed and contrast coding in the primary visual cortex of the cat

Adaptation is a ubiquitous property of the visual system. Adaptation often improves the ability to discriminate between stimuli and increases the operating range of the system, but is also associated with a reduced ability to veridically code stimulus attributes. Adaptation to luminance levels, contrast, orientation, direction and spatial frequency has been studied extensively, but knowledge about adaptation to image speed is less well understood. Here we examined how the speed tuning of neurons in cat primary visual cortex was altered after adaptation to speeds that were slow, optimal, or fast relative to each neuron's speed response function. We found that the preferred speed (defined as the speed eliciting the peak firing rate) of the neurons following adaptation was dependent on the speed at which they were adapted. At the population level cells showed decreases in preferred speed following adaptation to speeds at or above the non-adapted speed, but the preferred speed did not change following adaptation to speeds lower than the non-adapted peak. Almost all cells showed response gain control (reductions in absolute firing capacity) following speed adaptation. We also investigated the speed dependence of contrast adaptation and found that most cells showed contrast gain control (rightward shifts of their contrast response functions) and response gain control following adaptation at any speed. We conclude that contrast adaptation may produce the response gain control associated with speed adaptation, but shifts in preferred speed require an additional level of processing beyond contrast adaptation. A simple model is presented that is able to capture most of the findings.