Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings

Model-based characterization of the selectivity of neurons in primary visual cortex

Statistical models are increasingly being used to understand the complexity of stimulus selectivity in primary visual cortex (V1) in the context of complex time-varying stimuli, replacing averaging responses to simple parametric stimuli. Although such models often can more accurately reflect the computations performed by V1 neurons in more natural visual environments, they do not by themselves provide insight into V1 neural selectivity to basic stimulus features such as receptive field size, spatial frequency tuning, and phase invariance. Here, we present a battery of analyses that can be directly applied to encoding models to link complex encoding models to more interpretable aspects of stimulus selectivity. We apply this battery to nonlinear models of V1 neurons recorded in awake macaque during random bar stimuli. In linking model properties to more classical measurements, we demonstrate several novel aspects of V1 selectivity not available to simpler experimental measurements. For example, this approach reveals that individual spatiotemporal elements of the V1 models often have a smaller spatial scale than the neuron as a whole, resulting in nontrivial tuning to spatial frequencies. In addition, we propose measures of nonlinear integration that suggest that classical classifications of V1 neurons into simple versus complex cells will be spatial-frequency dependent. In total, rather than obfuscate classical characterizations of V1 neurons, model-based characterizations offer a means to more fully understand their selectivity, and link their classical tuning properties to their roles in more complex, natural, visual processing.NEW & NOTEWORTHY Visual neurons are increasingly being studied with more complex, natural visual stimuli, and increasingly complex models are necessary to characterize their response properties. Here, we describe a battery of analyses that relate these more complex models to classical characterizations. Using such model-based characterizations of V1 neurons furthermore yields several new insights into V1 processing not possible to capture in more classical means to measure their visual selectivity.

Spatiotemporal Content of Saccade Transients

Humans use rapid gaze shifts, known as saccades, to explore visual scenes. These movements yield abrupt luminance changes on the retina, which elicit robust neural discharges at fixation onsets. Yet little is known about the spatial content of saccade transients. Here, we show that saccades redistribute spatial information within the temporal range of retinal sensitivity following two distinct regimes: saccade modulations counterbalance (whiten) the spectral density of natural scenes at low spatial frequencies and follow the external power distribution at higher frequencies. This redistribution is a consequence of saccade dynamics, particularly the speed/amplitude/duration relation known as the main sequence. It resembles the redistribution resulting from inter-saccadic eye drifts, revealing a continuum in the modulations given by different eye movements, with oculomotor transitions primarily acting by regulating the bandwidth of whitening. Our findings suggest important computational roles for saccade transients in the establishment of spatial representations and lead to testable predictions about their consequences for visual functions and encoding mechanisms.

Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex

In rodents, the progression of extrastriate areas located laterally to primary visual cortex (V1) has been assigned to a putative object-processing pathway (homologous to the primate ventral stream), based on anatomical considerations. Recently, we found functional support for such attribution (Tafazoli et al., 2017), by showing that this cortical progression is specialized for coding object identity despite view changes, the hallmark property of a ventral-like pathway. Here, we sought to clarify what computations are at the base of such specialization. To this aim, we performed multielectrode recordings from V1 and laterolateral area LL (at the apex of the putative ventral-like hierarchy) of male adult rats, during the presentation of drifting gratings and noise movies. We found that the extent to which neuronal responses were entrained to the phase of the gratings sharply dropped from V1 to LL, along with the quality of the receptive fields inferred through reverse correlation. Concomitantly, the tendency of neurons to respond to different oriented gratings increased, whereas the sharpness of orientation tuning declined. Critically, these trends are consistent with the nonlinear summation of visual inputs that is expected to take place along the ventral stream, according to the predictions of hierarchical models of ventral computations and a meta-analysis of the monkey literature. This suggests an intriguing homology between the mechanisms responsible for building up shape selectivity and transformation tolerance in the visual cortex of primates and rodents, reasserting the potential of the latter as models to investigate ventral stream functions at the circuitry level.SIGNIFICANCE STATEMENT Despite the growing popularity of rodents as models of visual functions, it remains unclear whether their visual cortex contains specialized modules for processing shape information. To addresses this question, we compared how neuronal tuning evolves from rat primary visual cortex (V1) to a downstream visual cortical region (area LL) that previous work has implicated in shape processing. In our experiments, LL neurons displayed a stronger tendency to respond to drifting gratings with different orientations while maintaining a sustained response across the whole duration of the drift cycle. These trends match the increased complexity of pattern selectivity and the augmented tolerance to stimulus translation found in monkey visual temporal cortex, thus revealing a homology between shape processing in rodents and primates.

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.

Perception as a closed-loop convergence process

Perception of external objects involves sensory acquisition via the relevant sensory organs. A widely-accepted assumption is that the sensory organ is the first station in a serial chain of processing circuits leading to an internal circuit in which a percept emerges. This open-loop scheme, in which the interaction between the sensory organ and the environment is not affected by its concurrent downstream neuronal processing, is strongly challenged by behavioral and anatomical data. We present here a hypothesis in which the perception of external objects is a closed-loop dynamical process encompassing loops that integrate the organism and its environment and converging towards organism-environment steady-states. We discuss the consistency of closed-loop perception (CLP) with empirical data and show that it can be synthesized in a robotic setup. Testable predictions are proposed for empirical distinction between open and closed loop schemes of perception.

Conjectures regarding the nonlinear geometry of visual neurons

From the earliest stages of sensory processing, neurons show inherent non-linearities: the response to a complex stimulus is not a sum of the responses to a set of constituent basis stimuli. These non-linearities come in a number of forms and have been explained in terms of a number of functional goals. The family of spatial non-linearities have included interactions that occur both within and outside of the classical receptive field. They include, saturation, cross orientation inhibition, contrast normalization, end-stopping and a variety of non-classical effects. In addition, neurons show a number of facilitatory and invariance related effects such as those exhibited by complex cells (integration across position). Here, we describe an approach that attempts to explain many of the non-linearities under a single geometric framework. In line with Zetzsche and colleagues (e.g., Zetzsche et al., 1999) we propose that many of the principal non-linearities can be described by a geometry where the neural response space has a simple curvature. In this paper, we focus on the geometry that produces both increased selectivity (curving outward) and increased tolerance (curving inward). We demonstrate that overcomplete sparse coding with both low-dimensional synthetic data and high-dimensional natural scene data can result in curvature that is responsible for a variety of different known non-classical effects including end-stopping and gain control. We believe that this approach provides a more fundamental explanation of these non-linearities and does not require that one postulate a variety of explanations (e.g., that gain must be controlled or the ends of lines must be detected). In its standard form, sparse coding does not however, produce invariance/tolerance represented by inward curvature. We speculate on some of the requirements needed to produce such curvature.

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.

1-D Vision: Encoding of Eye Movements by Simple Receptive Fields

Eye movements (eyeM) are an essential component of visual perception. They allow the sampling and scanning of stationary scenes at various spatial scales, primarily at the scene level, via saccades, and at the local level, via fixational eyeM. Given the constant motion of visual images on the retina, a crucial factor in resolving spatial ambiguities related to the external scene is the exact trajectory of eyeM. We show here that the trajectory of eyeM can be encoded at high resolution by simple retinal receptive fields of the symmetrical type. We also show that such encoding can account for motion illusions such as the Ouchi illusion. In addition, encoding of motion projections along horizontal and vertical symmetrical simple retinal receptive fields entails a kind of Cartesian decomposition of the 2-D image into two 1-D projections.

Primate area V1: largest response gain for receptive fields in the straight-ahead direction

Although neuronal responses in behaving monkeys are typically studied while the monkey fixates straight ahead, it is known that eye position modulates responses of visual neurons. The modulation has been found to enhance neuronal responses when the receptive field is placed in the straight-ahead position for neurons receiving input from the peripheral but not the central retina. We studied the effect of eye position on the responses of V1 complex cells receiving input from the central retina (1.1-5.7° eccentricity) while minimizing the effect of fixational eye movements. Contrast response functions were obtained separately with drifting light and dark bars. Data were fit with the Naka-Rushton equation: r(c)=Rmax×c/(c+c50)+s, where r(c) is mean spike rate at contrast c, Rmax is the maximum response, c50 is the contrast that elicits half of Rmax, and s is the spontaneous activity. Contrast sensitivity as measured by c50 was not affected by eye position. For dark bars, there was a statistically significant decline in the normalized Rmax with increasing deviation from straight ahead. Data for bright bars showed a similar trend with a less rapid decline. Our results indicate that neurons representing the central retina show a bias for the straight-ahead position resulting from modulation of the response gain without an accompanying modulation of contrast sensitivity. The modulation is especially obvious for dark stimuli, which might be useful for directing attention to hazardous situations such as dark holes or shadows concealing important objects (Supplement 1: Video Abstract, Supplemental digital content 1, http://links.lww.com/WNR/A295).

Neurons in cat V1 show significant clustering by degree of tuning

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons recorded at a single site were significantly more similar in each of these properties than pairs of neurons from different recording sites. The strength of the clustering was generally modest. The percent decrease in the median difference between pairs from the same site, relative to pairs from different sites, was as follows: for different measures of orientation tuning width, 29-35% (drifting gratings) or 15-25% (flashed gratings); for DSI, 24%; and for spatial frequency tuning width measured in octaves, 8% (drifting gratings). The clusterings of all of these measures were much weaker than for preferred orientation (68% decrease) but comparable to that seen for preferred spatial frequency in response to drifting gratings (26%). For the above properties, little difference in clustering was seen between simple and complex cells. In studies of spatial frequency tuning to flashed gratings, strong clustering was seen among simple-cell pairs for tuning width (70% decrease) and preferred frequency (71% decrease), whereas no clustering was seen for simple-complex or complex-complex cell pairs.

A physiological perspective on fixational eye movements

For a behavioral neuroscientist, fixational eye movements are a double-edged sword. On one edge, they make control of visual stimuli difficult, but on the other edge they provide insight into the ways the visual system acquires information from the environment. We have studied macaque monkeys as models for human visual systems. Fixational eye movements of monkeys are similar to those of humans but they are more often vertically biased and spatially more dispersed. Eye movements scatter stimuli from their intended retinal locations, increase variability of neuronal responses, inflate estimates of receptive field size, and decrease measures of response amplitude. They also bias against successful stimulation of extremely selective cells. Compensating for eye movements reduced these errors and revealed a fine-grained motion pathway from V1 feeding the cortical ventral stream. Compensation is a useful tool for the experimenter, but rather than compensating for eye movements, the brain utilizes them as part of its input. The saccades and drifts that occur during fixation selectively activate different types of V1 neurons. Cells that prefer slower speeds respond during the drift periods with maintained discharges and tend to have smaller receptive fields that are selective for sign of contrast. They are well suited to code small details of the image and to enable our fine detailed vision. Cells that prefer higher speeds fire transient bursts of spikes when the receptive field leaves, crosses, or lands on a stimulus, but only the most transient ones (about one-third of our sample) failed to respond during drifts. Voluntary and fixational saccades had very similar effects, including the presence of a biphasic extraretinal modulation that interacted with stimulus-driven responses. Saccades evoke synchronous bursts that can enhance visibility but these bursts may also participate in the visual masking that contributes to saccadic suppression. Study of the small eye movements of fixation may illuminate some of the big problems in vision.

Human cortical areas involved in perception of surface glossiness

Glossiness is the visual appearance of an object's surface as defined by its surface reflectance properties. Despite its ecological importance, little is known about the neural substrates underlying its perception. In this study, we performed the first human neuroimaging experiments that directly investigated where the processing of glossiness resides in the visual cortex. First, we investigated the cortical regions that were more activated by observing high glossiness compared with low glossiness, where the effects of simple luminance and luminance contrast were dissociated by controlling the illumination conditions (Experiment 1). As cortical regions that may be related to the processing of glossiness, V2, V3, hV4, VO-1, VO-2, collateral sulcus (CoS), LO-1, and V3A/B were identified, which also showed significant correlation with the perceived level of glossiness. This result is consistent with the recent monkey studies that identified selective neural response to glossiness in the ventral visual pathway, except for V3A/B in the dorsal visual pathway, whose involvement in the processing of glossiness could be specific to the human visual system. Second, we investigated the cortical regions that were modulated by selective attention to glossiness (Experiment 2). The visual areas that showed higher activation to attention to glossiness than that to either form or orientation were identified as right hV4, right VO-2, and right V3A/B, which were commonly identified in Experiment 1. The results indicate that these commonly identified visual areas in the human visual cortex may play important roles in glossiness perception.

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.

Transformation of receptive field properties from lateral geniculate nucleus to superficial V1 in the tree shrew

Tree shrew primary visual cortex (V1) exhibits a pronounced laminar segregation of inputs from different classes of relay neurons in the lateral geniculate nucleus (LGN). We examined how several receptive field (RF) properties were transformed from LGN to V1 layer 4 to V1 layer 2/3. The progression of RF properties across these stages differed markedly from that found in the cat. V1 layer 4 cells are largely similar to the the LGN cells that provide their input, being dominated by a single sign (ON or OFF) and being strongly modulated by sinusoidal gratings. Some layer 4 neurons, notably those near the edges of layer 4, exhibited increased orientation selectivity, and most layer 4 neurons exhibited a preference for lower temporal frequencies. Neurons in cortical layer 2/3 differ significantly from those in the LGN; most exhibited strong orientation tuning and both ON and OFF responses. The strength of orientation selectivity exhibited a notable sublaminar organization, with the strongest orientation tuned neurons in the most superficial parts of layer 2/3. Modulation indexes provide evidence for simple and complex cells in both layer 4 and layer 2/3. However, neurons with high modulation indexes were heterogenous in the spatial organization of ON and OFF responses, with many of them exhibiting unbalanced ON and OFF responses rather than well-segregated ON and OFF subunits. When compared to the laminar organization of V1 in other mammals, these data show that the process of natural selection can result in significantly altered structure/function relationships in homologous cortical circuits.

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.

Seeing via Miniature Eye Movements: A Dynamic Hypothesis for Vision

During natural viewing, the eyes are never still. Even during fixation, miniature movements of the eyes move the retinal image across tens of foveal photoreceptors. Most theories of vision implicitly assume that the visual system ignores these movements and somehow overcomes the resulting smearing. However, evidence has accumulated to indicate that fixational eye movements cannot be ignored by the visual system if fine spatial details are to be resolved. We argue that the only way the visual system can achieve its high resolution given its fixational movements is by seeing via these movements. Seeing via eye movements also eliminates the instability of the image, which would be induced by them otherwise. Here we present a hypothesis for vision, in which coarse details are spatially encoded in gaze-related coordinates, and fine spatial details are temporally encoded in relative retinal coordinates. The temporal encoding presented here achieves its highest resolution by encoding along the elongated axes of simple-cell receptive fields and not across these axes as suggested by spatial models of vision. According to our hypothesis, fine details of shape are encoded by inter-receptor temporal phases, texture by instantaneous intra-burst rates of individual receptors, and motion by inter-burst temporal frequencies. We further describe the ability of the visual system to readout the encoded information and recode it internally. We show how reading out of retinal signals can be facilitated by neuronal phase-locked loops (NPLLs), which lock to the retinal jitter; this locking enables recoding of motion information and temporal framing of shape and texture processing. A possible implementation of this locking-and-recoding process by specific thalamocortical loops is suggested. Overall it is suggested that high-acuity vision is based primarily on temporal mechanisms of the sort presented here and low-acuity vision is based primarily on spatial mechanisms.

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.

Representation of surface luminance and contrast in primary visual cortex

In visual perception, object identification requires both the ability to define regions of uniform luminance and zones of luminance contrast. Neural processes underlying contrast detection have been well studied, while those defining luminance remain poorly understood and controversial. Partially because stimuli comprised of uniform luminance are relatively ineffective in driving responses of cortical neurons, little effort has been made to systematically compare responses of individual neurons to both uniform luminance and contrast. Using large static uniform luminance and contrast stimuli, modulated temporally in luminance or contrast, we found a continuum of responses ranging from a few cells modulated only by luminance (luminance-only), to many cells modulated by both luminance and contrast (luminance-contrast), and to many others modulated only by contrast (contrast-only) in primary visual cortex. Moreover, luminance-contrast cells had broader orientation tuning, larger receptive field (RF) and lower spatial frequency Preference, on average, than contrast-only cells. Contrast-only cells had contrast responses more linearly correlated to the spatial structure of their RFs than luminance-contrast cells. Taken together these results suggest that luminance and contrast are represented, to some degree, by independent mechanisms that may be shaped by different classes of subcortical and/or cortical inputs.

Population coding of visual space: modeling

We examine how the representation of space is affected by receptive field (RF) characteristics of the encoding population. Spatial responses were defined by overlapping Gaussian RFs. These responses were analyzed using multidimensional scaling to extract the representation of global space implicit in population activity. Spatial representations were based purely on firing rates, which were not labeled with RF characteristics (tuning curve peak location, for example), differentiating this approach from many other population coding models. Because responses were unlabeled, this model represents space using intrinsic coding, extracting relative positions amongst stimuli, rather than extrinsic coding where known RF characteristics provide a reference frame for extracting absolute positions. Two parameters were particularly important: RF diameter and RF dispersion, where dispersion indicates how broadly RF centers are spread out from the fovea. For large RFs, the model was able to form metrically accurate representations of physical space on low-dimensional manifolds embedded within the high-dimensional neural population response space, suggesting that in some cases the neural representation of space may be dimensionally isomorphic with 3D physical space. Smaller RF sizes degraded and distorted the spatial representation, with the smallest RF sizes (present in early visual areas) being unable to recover even a topologically consistent rendition of space on low-dimensional manifolds. Finally, although positional invariance of stimulus responses has long been associated with large RFs in object recognition models, we found RF dispersion rather than RF diameter to be the critical parameter. In fact, at a population level, the modeling suggests that higher ventral stream areas with highly restricted RF dispersion would be unable to achieve positionally-invariant representations beyond this narrow region around fixation.

Two-photon imaging of calcium in virally transfected striate cortical neurons of behaving monkey

Two-photon scanning microscopy has advanced our understanding of neural signaling in non-mammalian species and mammals. Various developments are needed to perform two-photon scanning microscopy over prolonged periods in non-human primates performing a behavioral task. In striate cortex in two macaque monkeys, cortical neurons were transfected with a genetically encoded fluorescent calcium sensor, memTNXL, using AAV1 as a viral vector. By constructing an extremely rigid and stable apparatus holding both the two-photon scanning microscope and the monkey's head, single neurons were imaged at high magnification for prolonged periods with minimal motion artifacts for up to ten months. Structural images of single neurons were obtained at high magnification. Changes in calcium during visual stimulation were measured as the monkeys performed a fixation task. Overall, functional responses and orientation tuning curves were obtained in 18.8% of the 234 labeled and imaged neurons. This demonstrated that the two-photon scanning microscopy can be successfully obtained in behaving primates.

Laminar and orientation-dependent characteristics of spatial nonlinearities: implications for the computational architecture of visual cortex

A full understanding of the computations performed in primary visual cortex is an important yet elusive goal. Receptive field models consisting of cascades of linear filters and static nonlinearities may be adequate to account for responses to simple stimuli such as gratings and random checkerboards, but their predictions of responses to complex stimuli such as natural scenes are only approximately correct. It is unclear whether these discrepancies are limited to quantitative inaccuracies that reflect well-recognized mechanisms such as response normalization, gain controls, and cross-orientation suppression or, alternatively, imply additional qualitative features of the underlying computations. To address this question, we examined responses of V1 and V2 neurons in the monkey and area 17 neurons in the cat to two-dimensional Hermite functions (TDHs). TDHs are intermediate in complexity between traditional analytic stimuli and natural scenes and have mathematical properties that facilitate their use to test candidate models. By exploiting these properties, along with the laminar organization of V1, we identify qualitative aspects of neural computations beyond those anticipated from the above-cited model framework. Specifically, we find that V1 neurons receive signals from orientation-selective mechanisms that are highly nonlinear: they are sensitive to phase correlations, not just spatial frequency content. That is, the behavior of V1 neurons departs from that of linear-nonlinear cascades with standard modulatory mechanisms in a qualitative manner: even relatively simple stimuli evoke responses that imply complex spatial nonlinearities. The presence of these findings in the input layers suggests that these nonlinearities act in a feedback fashion.

Visual receptive field structure of cortical inhibitory neurons revealed by two-photon imaging guided recording

Synaptic inhibition plays an important role in shaping receptive field (RF) properties in the visual cortex. However, the underlying mechanisms remain not well understood, partly because of difficulties in systematically studying functional properties of cortical inhibitory neurons in vivo. Here, we established two-photon imaging guided cell-attached recordings from genetically labeled inhibitory neurons and nearby "shadowed" excitatory neurons in the primary visual cortex of adult mice. Our results revealed that in layer 2/3, the majority of excitatory neurons exhibited both On and Off spike subfields, with their spatial arrangement varying from being completely segregated to overlapped. In contrast, most layer 4 excitatory neurons exhibited only one discernable subfield. Interestingly, no RF structure with significantly segregated On and Off subfields was observed for layer 2/3 inhibitory neurons of either the fast-spike or regular-spike type. They predominantly possessed overlapped On and Off subfields with a significantly larger size than the excitatory neurons and exhibited much weaker orientation tuning. These results from the mouse visual cortex suggest that different from the push-pull model proposed for simple cells, layer 2/3 simple-type neurons with segregated spike On and Off subfields likely receive spatially overlapped inhibitory On and Off inputs. We propose that the phase-insensitive inhibition can enhance the spatial distinctiveness of On and Off subfields through a gain control mechanism.

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.

Spectro-temporal modulation transfer function of single voxels in the human auditory cortex measured with high-resolution fMRI

Are visual and auditory stimuli processed by similar mechanisms in the human cerebral cortex? Images can be thought of as light energy modulations over two spatial dimensions, and low-level visual areas analyze images by decomposition into spatial frequencies. Similarly, sounds are energy modulations over time and frequency, and they can be identified and discriminated by the content of such modulations. An obvious question is therefore whether human auditory areas, in direct analogy to visual areas, represent the spectro-temporal modulation content of acoustic stimuli. To answer this question, we measured spectro-temporal modulation transfer functions of single voxels in the human auditory cortex with functional magnetic resonance imaging. We presented dynamic ripples, complex broadband stimuli with a drifting sinusoidal spectral envelope. Dynamic ripples are the auditory equivalent of the gratings often used in studies of the visual system. We demonstrate selective tuning to combined spectro-temporal modulations in the primary and secondary auditory cortex. We describe several types of modulation transfer functions, extracting different spectro-temporal features, with a high degree of interaction between spectral and temporal parameters. The overall low-pass modulation rate preference of the cortex matches the modulation content of natural sounds. These results demonstrate that combined spectro-temporal modulations are represented in the human auditory cortex, and suggest that complex signals are decomposed and processed according to their modulation content, the same transformation used by the visual system.

Optical imaging of contextual interactions in V1 of the behaving monkey

Interactions in primary visual cortex (V1) between simple visual elements such as short bar segments are believed to underlie our ability to easily integrate contours and segment surfaces. We used intrinsic signal optical imaging in alert fixating macaques to measure the strength and cortical distribution of V1 interactions among collinear bars. A single short bar stimulus produced a broad-peaked hill of activation (the optical point spread) covering multiple orientation hypercolumns in V1. Flanking the bar stimulus with a pair of identical collinear bars led to a strong nonlinear suppression in the optical signal. This nonlinearity was strongest over the center bar region, with a spatial distribution that cannot be explained by a simple gain control. It was a function of the relative orientation and separation of the bar stimuli in a manner tuned sharply for collinearity, being strongest for immediately adjacent bars lying on a smooth contour. These results suggest intracortical interactions playing a major role in determining V1 activation by smooth extended contours. Our finding that the interaction is primarily suppressive when imaged optically, which presumably reflects the combined inhibitory and excitatory inputs, suggests a complex interplay between these cortical inputs leading to the collinear facilitation seen in the spiking response of V1 neurons. This disjuncture between the facilitation seen in spiking and the suppression in imaging also suggests that cortical representations of complex stimuli involve interactions that need to be studied over extended networks and may be hard to deduce from the responses of individual neurons.

Adaptation to natural binocular disparities in primate V1 explained by a generalized energy model

Sensory processing in the brain is thought to have evolved to encode naturally occurring stimuli efficiently. We report an adaptation in binocular cortical neurons that reflects the tight constraints imposed by the geometry of 3D vision. We show that the widely used binocular energy model predicts that neurons dedicate part of their dynamic range to impossible combinations of left and right images. Approximately 42% of the neurons we record from V1 of awake monkeys behave in this way (a powerful confirmation of the model), while about 58% deviate from the model in a manner that concentrates more of their dynamic range on stimuli that obey the constraints of binocular geometry. We propose a simple extension of the energy model, using multiple subunits, that explains the adaptation we observe, as well as other properties of binocular neurons that have been hard to account for, such as the response to anti-correlated stereograms.

Physiological differences between neurons in layer 2 and layer 3 of primary visual cortex (V1) of alert macaque monkeys

The physiological literature does not distinguish between the superficial layers 2 and 3 of the primary visual cortex even though these two layers differ in their cytoarchitecture and anatomical connections. To distinguish layer 2 from layer 3, we have analysed the response characteristics of neurons recorded during microelectrode penetrations perpendicular to the cortical surface. Extracellular responses of single neurons to sweeping bars were recorded while macaque monkeys performed a fixation task. Data were analysed from penetrations where cells could be localized to specific depths in the cortex. Although the most superficial cells (depth, 145-371 microm; presumably layer 2) responded preferentially to particular stimulus orientations, they were less selective than cells encountered immediately beneath them (depth, 386-696 microm; presumably layer 3). Layer 2 cells had smaller spikes, higher levels of ongoing activity, larger receptive field activating regions, and less finely tuned selectivity for stimulus orientation and length than layer 3 cells. Direction selectivity was found only in layer 3. These data suggest that layer 3 is involved in generating and transmitting precise, localized information about image features, while the lesser selectivity of layer 2 cells may participate in top-down influences from higher cortical areas, as well as modulatory influences from subcortical brain regions.

Direction selectivity in V1 of alert monkeys: evidence for parallel pathways for motion processing

In primary visual cortex (V1) of macaque monkeys, motion selective cells form three parallel pathways. Two sets of direction selective cells, one in layer 4B, and the other in layer 6, send parallel direct outputs to area MT in the dorsal cortical stream. We show that these two outputs carry different types of spatial information. Direction selective cells in layer 4B have smaller receptive fields than those in layer 6, and layer 4B cells are more selective for orientation. We present evidence for a third direction selective pathway that flows through V1 layers 4Cm (the middle tier of layer 4C) to layer 3. Cells in layer 3 are very selective for orientation, have the smallest receptive fields in V1, and send direct outputs to area V2. Layer 3 neurons are well suited to contribute to detection and recognition of small objects by the ventral cortical stream, as well as to sense subtle motions within objects, such as changes in facial expressions.

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

One of the best-known dichotomies in neuroscience is the division of neurons in the mammalian primary visual cortex into simple and complex cells. Simple cells have receptive fields with separate on and off subregions and give phase-sensitive responses to moving gratings, whereas complex cells have uniform receptive fields and are phase invariant. The phase sensitivity of a cell is calculated as the ratio of the first Fourier coefficient ( F1) to the mean time-average ( F0) of the response to moving sinusoidal gratings at 100% contrast. Cells are then classified as simple ( F1/ F0>1) or complex ( F1/ F0<1). We manipulated cell responses by changing the stimulus contrast or through adaptation. The F1/ F0ratios of cells defined as complex at 100% contrast increased at low contrasts and following adaptation. Conversely, the F1/ F0ratios remained constant for cells defined as simple at 100% contrast. The latter cell type was primarily located in thalamorecipient layers 4 and 6. Many cells initially classified as complex exhibit F1/ F0>1 at low contrasts and after adaptation (particularly in layer 4). The results are consistent with the spike-threshold hypothesis, which suggests that the division of cells into two types arises from the nonlinear interaction of spike threshold with membrane potential responses.

A dynamic nonlinearity and spatial phase specificity in macaque V1 neurons

While studying the visual response dynamics of neurons in the macaque primary visual cortex (V1), we found a nonlinearity of temporal response that influences the visual functions of V1 neurons. Simple cells were recorded in all layers of V1; the nonlinearity was strongest in neurons located in layer 2/3. We recorded the spike responses to optimal sinusoidal gratings that were displayed for 100 ms, a temporal step response. The step responses were measured at many spatial phases of the grating stimulus. To judge whether simple cell behavior was consistent with linear temporal integration, the decay of the 100 ms step response at the preferred spatial phase was used to predict the step response at the opposite spatial phase. Responses in layers 4B and 4C were mostly consistent with a linear-plus-static-nonlinearity cascade model. However, this was not true in layer 2/3 where most cells had little or no step responses at the opposite spatial phase. Many layer 2/3 cells had transient preferred-phase responses but did not respond at the offset of the opposite-phase stimuli, indicating a dynamic nonlinearity. A different stimulus sequence, rapidly presented random sinusoids, also produced the same effect, with layer 2/3 simple cells exhibiting elevated spike rates in response to stimuli at one spatial phase but not 180 degrees away. The presence of a dynamic nonlinearity in the responses of V1 simple cells indicates that first-order analyses often capture only a fraction of neuronal behavior. The visual implication of our results is that simple cells in layer 2/3 are spatial phase-sensitive detectors that respond to contrast boundaries of one sign but not the opposite.

Image statistics and the perception of surface qualities

The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.

Eye position compensation improves estimates of response magnitude and receptive field geometry in alert monkeys

Studies of visual function in behaving subjects require that stimuli be positioned reliably on the retina in the presence of eye movements. Fixational eye movements scatter stimuli about the retina, inflating estimates of receptive field dimensions, reducing estimates of peak responses, and blurring maps of receptive field subregions. Scleral search coils are frequently used to measure eye position, but their utility for correcting the effects of fixational eye movements on receptive field maps has been questioned. Using eye coils sutured to the sclera and preamplifiers configured to minimize cable artifacts, we reexamined this issue in two rhesus monkeys. During repeated fixation trials, the eye position signal was used to adjust the stimulus position, compensating for eye movements and correcting the stimulus position to place it at the desired location on the retina. Estimates of response magnitudes and receptive field characteristics in V1 and in LGN were obtained in both compensated and uncompensated conditions. Receptive fields were narrower, with steeper borders, and response amplitudes were higher when eye movement compensation was used. In sum, compensating for eye movements facilitated more precise definition of the receptive field. We also monitored horizontal vergence over long sequences of fixation trials and found the variability to be low, as expected for this precise behavior. Our results imply that eye coil signals can be highly accurate and useful for optimizing visual physiology when rigorous precautions are observed.

The generation of receptive-field structure in cat primary visual cortex

Cells in primary visual cortex show a remarkable variety of receptive-field structures. In spite of the extensive experimental and theoretical effort over the past 50 years, it has been difficult to establish how this diversity of functional-response properties emerges in the cortex. One of the reasons is that while functional studies in the early visual pathway have been usually carried out in vivo with extracellular recording techniques, investigations about the precise structure of the cortical network have mainly been conducted in vitro. Thus, the link between structure and function has rarely been explicitly established, remaining a well-known controversial issue. In this chapter, I review recent data that simultaneously combines anatomy with physiology at the intracellular level; trying to understand how the primary visual cortex transforms the information it receives from the thalamus to generate receptive-field structure, contrast-invariant orientation tuning and other functional-response properties.

'Simplification' of responses of complex cells in cat striate cortex: suppressive surrounds and 'feedback' inactivation

In mammalian striate cortex (V1), two distinct functional classes of neurones, the so-called simple and complex cells, are routinely distinguished. They can be quantitatively differentiated from each other on the basis of the ratio between the phase-variant (F1) component and the mean firing rate (F0) of spike responses to luminance-modulated sinusoidal gratings (simple, F1/F0 > 1; complex, F1/F0 < 1). We investigated how recurrent cortico-cortical connections affect the spatial phase-variance of responses of V1 cells in the cat. F1/F0 ratios of the responses to optimally oriented drifting sine-wave gratings covering the classical receptive field (CRF) of single V1 cells were compared to those of: (1) responses to gratings covering the CRFs combined with gratings of different orientations presented to the 'silent' surrounds; and (2) responses to CRF stimulation during reversible inactivation of postero-temporal visual (PTV) cortex. For complex cells, the relative strength of the silent surround suppression on CRF-driven responses was positively correlated with the extent of increases in F1/F0 ratios. Inactivation of PTV cortex increased F1/F0 ratios of CRF-driven responses of complex cells only. Overall, activation of suppressive surrounds or inactivation of PTV 'converted' substantial proportions (50 and 30%, respectively) of complex cells into simple-like cells (F1/F0 > 1). Thus, the simple-complex distinction depends, at least partly, on information coming from the silent surrounds and/or feedback from 'higher-order' cortices. These results support the idea that simple and complex cells belong to the same basic cortical circuit and the spatial phase-variance of their responses depends on the relative strength of different synaptic inputs.

Laminar processing in the visual cortical column

Sensory regions of neocortex are organized as arrays of vertical columns composed of cells that share similar response properties, with the orientation columns of the cat's visual cortex being the best known example. Interest in how sensitivity to different stimulus features first emerges in the columns and how this selectivity is refined by subsequent processing has fueled decades of research. A natural starting point in approaching these issues is anatomy. Each column traverses the six cortical layers and each layer has a unique pattern of inputs, intrinsic connections and outputs. Thus, it makes sense to explore the possibility of corresponding laminar differences in sensory function, that is, to examine relationships between morphology and physiology. In addition, to help identify general patterns of cortical organization, it is useful to compare results obtained from different sensory systems and diverse species. The picture that emerges from such comparisons is that each cortical layer serves a distinct role in sensory function. Furthermore, different cortices appear to share some common strategies for processing information but also have specialized mechanisms adapted for the demands of specific sensory tasks.

Circuitry and the classification of simple and complex cells in V1

Based on a large-scale neural network model of striate cortex (V1), we present a simulation study of extra- and intracellular response modulations for drifting and contrast reversal grating stimuli. Specifically, we study the dependency of these modulations on the neural circuitry. We find that the frequently used ratio of the first harmonic to the mean response to classify simple and complex cells is highly insensitive to circuitry. Limited experimental sample size for the distribution of this measure makes it unsuitable for distinguishing whether the dichotomy of simple and complex cells originates from distinct LGN axon connectivity and/or local circuitry in V1. We show that a possible useful measure in this respect is the ratio of the intracellular second- to first-harmonic response for contrast reversal gratings. This measure is highly sensitive to neural circuitry and its distribution can be sampled with sufficient accuracy from a limited amount of experimental data. Further, the distribution of this measure is qualitatively similar to that of the subfield correlation coefficient, although it is more robust and easier to obtain experimentally.

Circuits that build visual cortical receptive fields

Neural sensitivity to basic elements of the visual scene changes dramatically as information is handed from the thalamus to the primary visual cortex in cats. Famously, thalamic neurons are insensitive to stimulus orientation whereas their cortical targets easily resolve small changes in stimulus angle. There are two main types of cells in the visual cortex, simple and complex, defined by the structure of their receptive fields. Simple cells are thought to lay the groundwork for orientation selectivity. This review focuses on approaches that combine anatomy with physiology at the intracellular level, to explore the circuits that build simple receptive fields and that help to maintain neural sensitivity to stimulus features even when luminance contrast changes.

High Response Reliability of Neurons in Primary Visual Cortex (V1) of Alert, Trained Monkeys

The reliability of neuronal responses determines the resources needed to represent the external world and constrains the nature of the neural code. Studies of anesthetized animals have indicated that neuronal responses become progressively more variable as information travels from the retina to the cortex. These results have been interpreted to indicate that perception must be based on pooling across relatively large numbers of cells. However, we find that in alert monkeys, responses in primary visual cortex (V1) are as reliable as the inputs from the retina and the thalamus. Moreover, when the effects of fixational eye movements were minimized, response variability (variance/mean - Fano factor, FF) in all V1 layers was low. When presenting optimal stimuli, the median FF was 0.3. High variability, FF approximately 1, was found only near threshold. Our results suggest that in natural vision, suprathreshold perception can be based on small numbers of optimally stimulated cells.

Responses of V1 neurons to two-dimensional hermite functions

Neurons in primary visual cortex are widely considered to be oriented filters or energy detectors that perform one-dimensional feature analysis. The main deviations from this picture are generally thought to include gain controls and modulatory influences. Here we investigate receptive field (RF) properties of single neurons with localized two-dimensional stimuli, the two-dimensional Hermite functions (TDHs). TDHs can be grouped into distinct complete orthonormal bases that are matched in contrast energy, spatial extent, and spatial frequency content but differ in two-dimensional form, and thus can be used to probe spatially specific nonlinearities. Here we use two such bases: Cartesian TDHs, which resemble vignetted gratings and checkerboards, and polar TDHs, which resemble vignetted annuli and dartboards. Of 63 isolated units, 51 responded to TDH stimuli. In 37/51 units, we found significant differences in overall response size (21/51) or apparent RF shape (28/51) that depended on which basis set was used. Because of the properties of the TDH stimuli, these findings are inconsistent with simple feedforward nonlinearities and with many variants of energy models. Rather, they imply the presence of nonlinearities that are not local in either space or spatial frequency. Units showing these differences were present to a similar degree in cat and monkey, in simple and complex cells, and in supragranular, infragranular, and granular layers. We thus find a widely distributed neurophysiological substrate for two-dimensional spatial analysis at the earliest stages of cortical processing. Moreover, the population pattern of tuning to TDH functions suggests that V1 neurons sample not only orientations, but a larger space of two-dimensional form, in an even-handed manner.

How close are we to understanding v1?

A wide variety of papers have reviewed what is known about the function of primary visual cortex. In this review, rather than stating what is known, we attempt to estimate how much is still unknown about V1 function. In particular, we identify five problems with the current view of V1 that stem largely from experimental and theoretical biases, in addition to the contributions of nonlinearities in the cortex that are not well understood. Our purpose is to open the door to new theories, a number of which we describe, along with some proposals for testing them.

Laminar organization of response properties in primary visual cortex of the gray squirrel (Sciurus carolinensis)

The gray squirrel (Sciurus carolinensis) is a diurnal highly visual rodent with a cone-rich retina. To determine which features of visual cortex are common to highly visual mammals and which are restricted to non-rodent species, we studied the laminar organization of response properties in primary visual area V1 of isoflurane-anesthetized squirrels using extra-cellular single-unit recording and sinusoidal grating stimuli. Of the responsive cells, 75% were tuned for orientation. Only 10% were directionally selective, almost all in layer 6, a layer receiving direct input from the dorsal lateral geniculate nucleus (LGN). Cone opponency was widespread but almost absent from layer 6. Median optimal spatial frequency tuning was 0.21 cycles/ degrees . Median optimal temporal frequency a high 5.3 Hz. Layer 4 had the highest percentage of simple cells and shortest latency (26 ms). Layers 2/3 had the lowest spontaneous activity and highest temporal frequency tuning. Layer 5 had the broadest spatial frequency tuning and most spontaneous activity. At the layer 4/5 border were sustained cells with high cone opponency. Simple cells, determined by modulation to drifting sinusoidal gratings, responded with shorter latencies, were more selective for orientation and direction, and were tuned to lower spatial frequencies. A comparison with other mammals shows that although the laminar organization of orientation selectivity is variable, the cortical input layers contain more linear cells in most mammals. Nocturnal mammals appear to have more orientation-selective neurons in V1 than diurnal mammals of similar size.

Temporal Properties of Inputs to Direction-Selective Neurons in Monkey V1

Motion in the visual scene is processed by direction-selective neurons in primary visual cortex. These cells receive inputs that differ in space and time. What are these inputs? A previous single-unit recording study in anesthetized monkey V1 proposed that the two major streams arising in the primate retina, the M and P pathways, differed in space and time as required to create direction selectivity. We confirmed that cortical cells driven by P inputs tend to have sustained responses. The M pathway, however, as assessed by recordings in layer 4Cα and from cells with high contrast sensitivity, is not purely transient. The diversity of timing in the M stream suggests that combinations of M inputs, as well as of M and P inputs, create direction selectivity.

On the Division of Cortical Cells Into Simple and Complex Types: A Comparative Viewpoint

Hubel and Weisel introduced the concept of cells in cat primary visual cortex being partitioned into two categories: simple and complex. Subsequent authors have developed a quantitative measure to distinguish the two cell types based on the ratio between modulated responses at the stimulus frequency ( F1) and unmodulated ( F0) components of the spiking responses to drifting sinusoidal gratings. It has been shown that cells in anesthetized cat and monkey cortex have bimodal distributions of F1/ F0ratios. A clear local minimum or dip exists in the distribution at a ratio close to unity. Here we present a comparison of the distributions of the F1/ F0ratios between cells in the primary visual cortex of the eutherian cat and marsupial Tammar wallaby, Macropus eugenii. This is the first quantitative description of any marsupial cortex using the F1/ F0ratio and follows earlier papers showing that cells in wallaby cortex are tightly oriented and spatial frequency tuned. The results reveal a bimodal distribution in the wallaby F1/ F0ratios that is very similar to that found in the rat, cat, and monkey. Discussion focuses on the mechanisms that could lead to such similar cell distributions in animals with diverse behaviors and phylogenies.