Extraclassical Receptive Field Phenomena and Short-Range Connectivity in V1

A comprehensive data-driven model of cat primary visual cortex

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue in the domain of the visual system: a comprehensive spiking model of cat primary visual cortex. The presented model satisfies an extensive range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception.

Generalizing biological surround suppression based on center surround similarity via deep neural network models

Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs.

Saturation of visual responses explains size tuning in rat collicular neurons

The receptive field of many visual neurons is composed of a central responsive area, the classical receptive field, and a non-classical receptive field, also called the "suppressive surround." A visual stimulus placed in the suppressive surround does not induce any response but modulates visual responses to stimuli within the classical receptive field, usually by suppressing them. Therefore, visual responses become smaller when stimuli exceed the classical receptive field size. The stimulus size inducing the maximal response is called the preferred stimulus size. In cortex, there is good correspondence between the sizes of the classical receptive field and the preferred stimulus. In contrast, in the rodent superior colliculus, the preferred size is often several fold smaller than the classical receptive field size. Here, we show that in the rat superior colliculus, the preferred stimulus size changes as a square root of the contrast inverse and the classical receptive field size is independent of contrast. In addition, responses to annulus were largely independent of the inner hole size. To explain these data, three models were tested: the divisive modulation of the gain by the suppressive surround (the "normalization" model), the difference of the Gaussians, and a divisive model that incorporates saturation to light flux. Despite the same number of free parameters, the model incorporating saturation to light performed the best. Thus, our data indicate that in rats, the saturation to light can be a dominant phenomenon even at relatively low illumination levels defining visual responses in the collicular neurons.

Evidence for the intrinsically nonlinear nature of receptive fields in vision

The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). The linear RF has a number of inherent problems: it changes with the input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations. Here we propose to model the RF in a nonlinear manner, introducing the intrinsically nonlinear receptive field (INRF). Apart from being more physiologically plausible and embodying the efficient representation principle, the INRF has a key property of wide-ranging implications: for several vision science phenomena where a linear RF must vary with the input in order to predict responses, the INRF can remain constant under different stimuli. We also prove that Artificial Neural Networks with INRF modules instead of linear filters have a remarkably improved performance and better emulate basic human perception. Our results suggest a change of paradigm for vision science as well as for artificial intelligence.

A phase mixing model for the frequency-doubling illusion

We introduce a temporal phase mixing model for a description of the frequency-doubling illusion (FDI). The model is generic in the sense that it can be set to refer to retinal ganglion cells, lateral geniculate cells, as well as simple cells in the primary visual cortex (V1). Model parameters, however, strongly suggest that the FDI originates in the cortex. The model shows how noise in the response phases of cells in V1, or in further processing of these phases, easily produces observed behavior of FDI onset as a function of spatiotemporal frequencies. It also shows how this noise can accommodate physiologically plausible spatial delays in comparing neural signals over a distance. The model offers an explanation for the disappearance of the FDI at sufficiently high spatial frequencies via increasingly correlated coding of neighboring grating stripes. Further, when the FDI is equated to vanishing perceptual discrimination between asynchronous contrast-reversal gratings, the model proposes the possibility that the FDI shows a resonance behavior at sufficiently high spatial frequencies, by which it is alternately perceived and not perceived in sequential temporal frequency bands.

Perceptual decision making "through the eyes" of a large-scale neural model of v1

Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given activity generated by a realistic mapping of the visual scene to neuronal spike trains, how do downstream neurons best utilize this representation to generate a “decision.” Specifically we consider both sparse (L1-regularized) and non-sparse (L2 regularized) linear decoding for mapping the neural dynamics of a large-scale spiking neuron model of primary visual cortex (V1) to a two alternative forced choice (2-AFC) perceptual decision. We show that while both sparse and non-sparse linear decoding yield discrimination results quantitatively consistent with human psychophysics, sparse linear decoding is more efficient in terms of the number of selected informative dimension.

Sharper orientation tuning of the extraclassical suppressive-surround due to a neuron's location in the V1 orientation map emerges late in time

Neuronal responses in primary visual cortex (V1) can be suppressed by a stimulus presented to the extraclassical surround, and such interactions are thought to be critical for figure ground segregation and form perception. While surround suppression likely originates from both feedforward afferents and multiple cortical circuits, it is unclear what role each circuit plays in the surround's orientation tuning. To investigate this we recorded from single units in V1 of anesthetized cat and analyzed the orientation tuning of the suppressive-surround over time. In addition, based on orientation maps derived through optical imaging prior to recording, neurons were classified as being located in domains or pinwheels. For both types of neurons, shortly after response onset (10 ms) the suppressive-surround is broadly tuned to orientation, but this is followed by a steep improvement in tuning over the next ∼30 ms. While the tuning of the pinwheel cells plateaus at this point, tuning is enhanced further for domain cells, especially those located superficially in the cortex, reaching a peak at 80 ms from response onset. This relatively slow evolution of the orientation tuning of the suppressive surround suggests that fast-arriving feedforward circuits (10 ms) likely only provide broadly tuned suppression, but that feedback from higher visual areas which is likely to arrive over the next 30 ms and can cover both the receptive field center and the extraclassical surround contributes to the initial steep rise in tuning for both cell types. Moreover, we speculate that the even later enhancement in tuning for domain neurons could mean the involvement of inputs from relatively long-range lateral connections, which not only propagate slowly but also link like-oriented domains corresponding to the receptive field of only the extraclassical surround.

Effects of cortical feedback on the spatial properties of relay cells in the lateral geniculate nucleus

Feedback connections to early-level sensory neurons have been shown to affect many characteristics of their neural response. Because selectivity for stimulus size is a fundamental property of visual neurons, we examined the summation tuning and discretely mapped receptive field (RF) properties of cells in the lateral geniculate nucleus (LGN) both with and without feedback from visual cortex. Using extracellular recording in halothane-anesthetized cats, we used small luminance probes displaced in Cartesian coordinates to measure discrete response area, and optimal sinusoidal gratings of varying diameter to estimate preferred optimal summation size and level of center-surround antagonism. In conditions where most cortical feedback was pharmacologically removed, discretely mapped RF response areas showed an overall significant enlargement for the population compared with control conditions. A switch to increased levels of burst firing, spatially displaced from the RF center, suggested this was mediated by changes in excitatory-inhibitory balance across visual space. With the use of coextensive stimulation, there were overall highly significant increases in the optimal summation size and reduction of surround antagonism with removal of cortical feedback in the LGN. When fitted with a difference-of-Gaussian (DOG) model, changes in the center size, center amplitude, and surround amplitude parameters were most significantly related to the removal of cortical feedback. In summary, corticothalamic innervation of the visual thalamus can modify spatial summation properties in LGN relay cells, an effect most parsimoniously explained by changes in the excitatory-inhibitory balance.

Strong recurrent networks compute the orientation tuning of surround modulation in the primate primary visual cortex

In macaque primary visual cortex (V1), neuronal responses to stimuli inside the receptive field (RF) are modulated by stimuli in the RF surround. This modulation is orientation specific. Previous studies suggested that, for some cells, this specificity may not be fixed but changes with the stimulus orientation presented to the RF. We demonstrate, in recording studies, that this tuning behavior is instead highly prevalent in V1 and, in theoretical work, that it arises only if V1 operates in a regime of strong local recurrence. Strongest surround suppression occurs when the stimuli in the RF and the surround are iso-oriented, and strongest facilitation when the stimuli are cross-oriented. This is the case even when the RF is suboptimally activated by a stimulus of nonpreferred orientation but only if this stimulus can activate the cell when presented alone. This tuning behavior emerges from the interaction of lateral inhibition (via the surround pathways), which is tuned to the preferred orientation of the RF, with weakly tuned, but strong, local recurrent connections, causing maximal withdrawal of recurrent excitation at the feedforward input orientation. Thus, horizontal and feedback modulation of strong recurrent circuits allows the tuning of contextual effects to change with changing feedforward inputs.

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration

Age-related macular degeneration (AMD) is the major cause of blindness in the developed world. Though substantial work has been done to characterize the disease, it is difficult to predict how the state of an individual's retina will ultimately affect their high-level perceptual function. In this paper, we describe an approach that couples retinal imaging with computational neural modeling of early visual processing to generate quantitative predictions of an individual's visual perception. Using a patient population with mild to moderate AMD, we show that we are able to accurately predict subject-specific psychometric performance by decoding simulated neurodynamics that are a function of scotomas derived from an individual's fundus image. On the population level, we find that our approach maps the disease on the retina to a representation that is a substantially better predictor of high-level perceptual performance than traditional clinical metrics such as drusen density and coverage. In summary, our work identifies possible new metrics for evaluating the efficacy of treatments for AMD at the level of the expected changes in high-level visual perception and, in general, typifies how computational neural models can be used as a framework to characterize the perceptual consequences of early visual pathologies.

Predicted contextual modulation varies with distance from pinwheel centers in the orientation preference map

In the primary visual cortex (V1) of some mammals, columns of neurons with the full range of orientation preferences converge at the center of a pinwheel-like arrangement, the ‘pinwheel center' (PWC). Because a neuron receives abundant inputs from nearby neurons, the neuron's position on the cortical map likely has a significant impact on its responses to the layout of orientations inside and outside its classical receptive field (CRF). To understand the positional specificity of responses, we constructed a computational model based on orientation preference maps in monkey V1 and hypothetical neuronal connections. The model simulations showed that neurons near PWCs displayed weaker but detectable orientation selectivity within their CRFs, and strongly reduced contextual modulation from extra-CRF stimuli, than neurons distant from PWCs. We suggest that neurons near PWCs robustly extract local orientation within their CRF embedded in visual scenes, and that contextual information is processed in regions distant from PWCs.

Beyond multiple pattern analyzers modeled as linear filters (as classical V1 simple cells): useful additions of the last 25 years

This review briefly discusses processes that have been suggested in the last 25 years as important to the intermediate stages of visual processing of patterns. Five categories of processes are presented: (1) Higher-order processes including FRF structures; (2) Divisive contrast nonlinearities including contrast normalization; (3) Subtractive contrast nonlinearities including contrast comparison; (4) Non-classical receptive fields (surround suppression, cross-orientation inhibition); (5) Contour integration.

Surround suppression by high spatial frequency stimuli in the cat primary visual cortex

Surround suppression is a phenomenon whereby stimulation of the extraclassical receptive field suppressively modulates the visual responses of neurons in the primary visual cortex (V1) (also known as area 17). It is known that surround suppression tunes to spatial frequencies (SFs) that are much lower and broader than the frequencies to which the classical receptive field tunes. In this study, we tested the effects of varying SFs on surround suppression by using a circular sinusoidal grating patch that covered both the classical receptive field and the extraclassical receptive field. Using area-summation tuning curves, we found high-SF-tuned surround suppression in the cat V1. This high-SF-tuned surround suppression causes the SF tuning to shift to low SF for large stimuli. By simulating a model neuron lacking a suppressive surround mechanism, we confirmed that these preferred SF shifts do not occur in the absence of surround suppression. We surmise that the high-SF-tuned suppression, which shifts the preferred SF according to size, functionally contributes to the scale-invariant processing of visual images in V1.

Mapping of contextual modulation in the population response of primary visual cortex

We review the evidence of long-range contextual modulation in V1. Populations of neurons in V1 are activated by a wide variety of stimuli outside of their classical receptive fields (RF), well beyond their surround region. These effects generally involve extra-RF features with an orientation component. The population mapping of orientation preferences to the upper layers of V1 is well understood, as far as the classical RF properties are concerned, and involves organization into pinwheel-like structures. We introduce a novel hypothesis regarding the organization of V1's contextual response. We show that RF and extra-RF orientation preferences are mapped in related ways. Orientation pinwheels are the foci of both types of features. The mapping of contextual features onto the orientation pinwheel has a form that recapitulates the organization of the visual field: an iso-orientation patch within the pinwheel also responds to extra-RF stimuli of the same orientation. We hypothesize that the same form of mapping applies to other stimulus properties that are mapped out in V1, such as colour and contrast selectivity. A specific consequence is that fovea-like properties will be mapped in a systematic way to orientation pinwheels. We review the evidence that cytochrome oxidase blobs comprise the foci of this contextual remapping for colour and low contrasts. Neurodynamics and motion in the visual field are argued to play an important role in the shaping and maintenance of this type of mapping in V1.

Correlation between spatial frequency and orientation selectivity in V1 cortex: implications of a network model

We addressed how spatial frequency and orientation selectivity coexist and co-vary in Macaque primary visual cortex (V1) by simulating cortical layer 4Cα of V1 with a large-scale network model and then comparing the model's behavior with a population of cells we recorded in layer 4Cα. We compared the distributions of orientation and spatial frequency selectivity, as well as the correlation between the two, in the model with what we observed in the 4Cα population. We found that (1) in the model, both spatial frequency and orientation selectivity of neuronal firing are greater and more diverse than the LGN inputs to model neurons; (2) orientation and spatial frequency selectivity co-vary in the model in a way very similar to what we observed in layer 4Cα neurons; (3) in the model, orientation and spatial frequency selectivity co-vary because of intra-cortical inhibition. The results suggest that cortical inhibition provides a common mechanism for selectivity in multiple dimensions.

Spatial attention in area V4 is mediated by circuits in primary visual cortex

The ability to covertly select visual stimuli in our environment based on their behavioral relevance is an important skill. Stimulus selection has been studied experimentally, at the single neuron as well as at the population level, by recording from the visual cortex of subjects performing attention-demanding tasks, but studies at the local circuit level are lacking. We conducted simulations of a primary visual cortex (V1) model to provide insight into the local circuit computation underlying stimulus selection in V4. Two small oriented rectangular bars were placed at different locations in the 4 by 4 degree visual field represented by the V1 model, such that they activated different V1 neurons but such that they were both inside the classical receptive field (CRF) of the same V4 neuron. The biased competition framework [Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193-222] makes predictions for the response of V4 neurons and the modulation thereof by spatial and feature attention. In our simulation of the V1 network, we obtained results consistent with these predictions for V4 when the model had long-range excitatory projections targeting inhibitory neurons and when spatial attention was mediated by a spatially restricted projection that either inhibited the inhibitory neurons or excited the excitatory neurons. Although it is not clear whether attention effects measured in V4 neurons are generated mostly by local circuits within V4, our simulations suggest that spatial attention at a resolution less than the size of the CRF of a V4 neuron is inherited from upstream areas like V1 and relies on circuits mediating surround suppression at the single neuron level. Furthermore, the model displayed global oscillations in the alpha frequency range (around 10 Hz), whose coherence was highest in the absence of visual stimulation, which is consistent with electroencephalograms recorded in humans. By contrast, when a stimulus was presented the alpha oscillation sped up and became less coherent, whereas at the single column level (40-480 cells) transient beta/gamma oscillations were observed with a frequency between 25 and 50 Hz.

Quantitative multifocal fMRI shows active suppression in human V1

Multifocal functional magnetic resonance imaging has recently been introduced as an alternative method for retinotopic mapping, and it enables effective functional localization of multiple regions-of-interest in the visual cortex. In this study we characterized interactions in V1 with spatially and temporally identical stimuli presented alone, or as a part of a nine-region multifocal stimulus. We compared stimuli at different contrasts, collinear and orthogonal orientations and spatial frequencies one octave apart. Results show clear attenuation of BOLD signal from the central region in the multifocal condition. The observed modulation in BOLD signal could be produced either by neural suppression resulting from stimulation of adjacent regions of visual field, or alternatively by hemodynamic saturation or stealing effects in V1. However, we find that attenuation of the central response persists through a range of contrasts, and that its strength varies with relative orientation and spatial frequency of the central and surrounding stimulus regions, indicating active suppression mechanisms of neural origin. Our results also demonstrate that the extent of the signal spreading is commensurate with the extent of the horizontal connections in primate V1.

'Top-down' influences of ipsilateral or contralateral postero-temporal visual cortices on the extra-classical receptive fields of neurons in cat's striate cortex

In anesthetized and immobilized domestic cats, we have studied the effects of brief reversible inactivation (by cooling to 10 degrees C) of the ipsilateral or contralateral postero-temporal visual (PTV) cortices on: 1) the magnitude of spike-responses of neurons in striate cortex (cytoarchitectonic area 17, area V1) to optimized sine-wave modulated contrast-luminosity gratings confined to the classical receptive fields (CRFs) and 2) the relative strengths of modulation of CRF-induced spike-responses by gratings extending into the extra-classical receptive field (ECRF). Consistent with our previous reports (Bardy et al., 2006; Huang et al., 2007), inactivation of ipsilateral PTV cortex (presumed homologue of primate infero-temporal cortex) resulted in significant reversible changes (almost all substantial reductions) in the magnitude of spike-responses to CRF-confined stimuli in about half of the V1 neurones. Similarly, in half of the present sample, inactivation of ipsilateral PTV cortex resulted in significant reversible changes (in over 70% of cases, reduction) in the relative strength of ECRF modulation of the CRF-induced spike-responses. By contrast, despite the fact that receptive fields of all V1 cells tested were located within 5 degrees of representation of the zero vertical meridian, inactivation of contralateral PTV cortex only rarely resulted in significant (yet invariably small) changes in the magnitude of spike-responses to CRF-confined stimuli or significant (again invariably small) changes in the relative strength of ECRF modulation of spike-responses. Thus, the ipsilateral, but not contralateral, 'higher-order' visual cortical areas make significant contribution not only to the magnitude of CRF-induced spike-responses but also to the relative strengths of ECRF-induced modulation of the spike-responses of V1 neurons. Therefore, the feedback signals originating from the ipsilateral higher-order cortical areas appear to make an important contribution to contextual modulation of responses of neurons in the primary visual cortices.

A model of contour extraction including multiple scales, flexible inhibition and attention

A mathematical model of contextual integration and contour extraction in the primary visual cortex developed in a recent work [Ursino, M., & La Cara, G. E. (2004). A model of contextual interactions and contour detection in primary visual cortex. Neural Networks, 17, 719-735] has been significantly improved to include two fundamental additional aspects, i.e., multi-scale decomposition and attention. The model incorporates two independent paths for visual processing corresponding to two different scales. Attention from higher hierarchical levels works by modifying different properties of the network: by selecting the portion of the image to be scrutinized and the appropriate scale, by modulating the threshold of a gating mechanism, and by modifying the width and/or strength of lateral inhibition. Through computer simulations of real complex and noisy black-and-white images, we demonstrate that appropriate selection of the above factors allows accurate analysis of image contours at different levels, from global perception of the overall objects without details, down to a fine examination of minute particulars (such as the lips in a face or the fingers of a hand). Attentive reconfiguration of lateral inhibition plays a key role in the analysis of images at different detail levels.

Dependence of response properties on sparse connectivity in a spiking neuron model of the lateral geniculate nucleus

We present a large-scale anatomically constrained spiking neuron model of the lateral geniculate nucleus (LGN), which operates solely with retinal input, relay cells, and interneurons. We show that interneuron inhibition and sparse connectivity between LGN cells could be key factors for explaining a number of observed classical and extraclassical response properties in LGN of monkey and cat. Among them are 1) weak orientation tuning, 2) contrast invariance of spatial frequency tuning in the absence of cortical feedback, 3) extraclassical surround suppression, and 4) orientation tuning of extraclassical surround suppression. The model also makes two surprising predictions: 1) a possible pinwheel-like spatial organization of orientation preference in the parvo layers of monkey LGN, much like what is seen in V1, and 2) a stimulus-induced trend (bias) in the orientation and phase preference of surround suppression, originating from the stimulus discontinuity between center and surround gratings rather than from specific circuitry.

Two expressions of "surround suppression" in V1 that arise independent of cortical mechanisms of suppression

The preferred stimulus size of a V1 neuron decreases with increases in stimulus contrast. It has been supposed that stimulus contrast is the primary determinant of such spatial summation in V1 cells, though the extent to which it depends on other stimulus attributes such as orientation and spatial frequency remains untested. We investigated this by recording from single cells in V1 of anaesthetized cats and monkeys, measuring size-tuning curves for high-contrast drifting gratings of optimal spatial configuration, and comparing these curves with those obtained at lower contrast or at sub-optimal orientations or spatial frequencies. For drifting gratings of optimal spatial configuration, lower contrasts produced less surround suppression resulting in increases in preferred size. High contrast gratings of sub-optimal spatial configuration produced more surround suppression than optimal low-contrast gratings, and as much or more surround suppression than optimal high-contrast gratings. For sub-optimal spatial frequencies, preferred size was similar to that for the optimal high-contrast stimulus, whereas for sub-optimal orientations, preferred size was smaller than that for the optimal high-contrast stimulus. These results indicate that, while contrast is an important determinant of spatial summation in V1, it is not the only determinant. Simulation of these experiments on a cortical receptive field modeled as a Gabor revealed that the small preferred sizes observed for non-preferred stimuli could result simply from linear filtering by the classical receptive field. Further simulations show that surround suppression in retinal ganglion cells and LGN cells can be propagated to neurons in V1, though certain properties of the surround seen in cortex indicate that it is not solely inherited from earlier stages of processing.

Space and time in visual context

No sensory stimulus is an island unto itself; rather, it can only properly be interpreted in light of the stimuli that surround it in space and time. This can result in entertaining illusions and puzzling results in psychological and neurophysiological experiments. We concentrate on perhaps the best studied test case, namely orientation or tilt, which gives rise to the notorious tilt illusion and the adaptation tilt after-effect. We review the empirical literature and discuss the computational and statistical ideas that are battling to explain these conundrums, and thereby gain favour as more general accounts of cortical processing.

Suppressive surrounds and contrast gain in magnocellular-pathway retinal ganglion cells of macaque

The modulation sensitivity of visual neurons can be influenced by remote stimuli which, when presented alone, cause no change in the ongoing discharge rate of the neuron. We show here that the extraclassical surrounds that underlie these effects are present in magnocellular-pathway (MC) but not in parvocellular-pathway (PC) retinal ganglion cells of the macaque. The response of MC cells to drifting gratings and flashing spots was halved by drifting or contrast-reversing gratings surrounding their receptive fields, but PC cell responses were unaffected. The suppression cannot have arisen from the classical receptive field, or been caused by scattered light, because it could be evoked by annuli that themselves caused little or no response from the cell, and is consistent with the action of a divisive suppressive mechanism. Suppression in MC cells was broadly tuned for spatial and temporal frequency and greater at high contrast. If perceptual phenomena with similar stimulus contexts, such as the "shift effect" and saccadic suppression, have a retinal component, then they reflect the activity of the MC pathway.

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.

Dynamics of suppression in macaque primary visual cortex

The response of a neuron in primary visual cortex (V1) to an optimal stimulus in its classical receptive field (CRF) can be reduced by the presence of an orthogonal mask, a phenomenon known as cross-orientation suppression. The presence of a parallel stimulus outside the CRF can have a similar effect, in this case known as surround suppression. We used a novel stimulus to probe the time course of cross-orientation suppression and found that it is very fast, starting even before the response to optimal excitatory stimuli. However, it occurs with some delay after the offset response, considered to be a measure of the earliest excitatory signals that reach the CRF. We also examined the time course of response to a stimulus presented outside the CRF and found that cross-orientation suppression begins substantially earlier than surround suppression measured in the same cells. Together, these findings suggest that cross-orientation suppression is attributable to either direct feedforward signal paths to V1 neurons or a circuit involving fast local interneurons within V1. Feedback from higher cortical areas is implicated in surround suppression, but our results make this an implausible mechanism for cross-orientation suppression. We conclude that suppression from inside and outside the CRF occur through different mechanisms.

Analysis of a gain control model of V1: is the goal redundancy reduction?

In this paper we analyze a popular divisive normalization model of V1 with respect to the relationship between its underlying coding strategy and the extraclassical physiological responses of its constituent modeled neurons. Specifically we are interested in whether the optimization goal of redundancy reduction naturally leads to reasonable neural responses, including reasonable distributions of responses. The model is trained on an ensemble of natural images and tested using sinusoidal drifting gratings, with metrics such as suppression index and contrast dependent receptive field growth compared to the objective function values for a sample of neurons. We find that even though the divisive normalization model can produce "typical" neurons that agree with some neurophysiology data, distributions across samples do not agree with experimental data. Our results suggest that redundancy reduction itself is not necessarily causal of the observed extraclassical receptive field phenomena, and that additional optimization dimensions and/or biological constraints must be considered.

Visual receptive field organization

Increasingly systematic approaches to quantifying receptive fields in primary visual cortex, combined with inspired ideas about functional circuitry, non-linearities, and visual stimuli, are bringing new interest to classical problems. This includes the distinction and hierarchy between simple and complex cells, the mechanisms underlying the receptive field surround, and debates about optimal stimuli for mapping receptive fields. An important new problem arises from recent observations of stimulus-dependent spatial and temporal summation in primary visual cortex. It appears that the receptive field can no longer be considered unique, and we might have to relinquish this cherished notion as the embodiment of neuronal function in primary visual cortex.