Membrane potential and conductance changes underlying length tuning of cells in cat primary visual cortex

How auditory neurons count temporal intervals and decode information

The numerical sense of animals includes identifying the numerosity of a sequence of events that occur with specific intervals, e.g., notes in a call or bar of music. Across nervous systems, the temporal patterning of spikes can code these events, but how this information is decoded (counted) remains elusive. In the anuran auditory system, temporal information of this type is decoded in the midbrain, where "interval-counting" neurons spike only after at least a threshold number of sound pulses have occurred with specific timing. We show that this decoding process, i.e., interval counting, arises from integrating phasic, onset-type and offset inhibition with excitation that augments across successive intervals, possibly due to a progressive decrease in "shunting" effects of inhibition. Because these physiological properties are ubiquitous within and across central nervous systems, interval counting may be a general mechanism for decoding diverse information coded/encoded in temporal patterns of spikes, including "bursts," and estimating elapsed time.

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.

Object Boundary Detection in Natural Images May Depend on "Incitatory" Cell-Cell Interactions

Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional "simple cells," we labeled 30,000 natural image patches and used Bayes' rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified the following three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, and nonmonotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models, we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition-a compound effect we call "incitation"-can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer "delta" rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems.SIGNIFICANCE STATEMENT Simple cells in primary visual cortex (V1) respond to oriented edges and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell-a divisively normalized linear filter-is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

What is the dynamical regime of cerebral cortex?

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.

How auditory selectivity for sound timing arises: The diverse roles of GABAergic inhibition in shaping the excitation to interval-selective midbrain neurons

Across sensory systems, temporal frequency information is progressively transformed along ascending central pathways. Despite considerable effort to elucidate the mechanistic basis of these transformations, they remain poorly understood. Here we used a novel constellation of approaches, including whole-cell recordings and focal pharmacological manipulation, in vivo, and new computational algorithms that identify conductances resulting from excitation, inhibition and active membrane properties, to elucidate the mechanisms underlying the selectivity of midbrain auditory neurons for long temporal intervals. Surprisingly, we found that stimulus-driven excitation can be increased and its selectivity decreased following attenuation of inhibition with gabazine or intracellular delivery of fluoride. We propose that this nonlinear interaction is due to shunting inhibition. The rate-dependence of this inhibition results in the illusion that excitation to a cell shows greater temporal selectivity than is actually the case. We also show that rate-dependent depression of excitation, an important component of long-interval selectivity, can be decreased after attenuating inhibition. These novel findings indicate that nonlinear shunting inhibition plays a key role in shaping the amplitude and interval selectivity of excitation. Our findings provide a major advance in understanding how the brain decodes intervals and may explain paradoxical temporal selectivity of excitation to midbrain neurons reported previously.

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice

The synaptic mechanisms of feature coding in the visual cortex are poorly understood, particularly in awake animals. The ratio between excitation (E) and inhibition (I) might be constant across stimulus space, controlling only the gain and timing of neuronal responses, or it might change, directly contributing to feature coding. Whole-cell recordings in L2/3 of awake mice revealed that the E/I ratio systematically declines with increasing stimulus contrast or size. Suppressing somatostatin (SOM) neurons enhanced the E and I underlying size tuning, explaining SOM neurons' role in surround suppression. These data imply that contrast and size tuning result from a combination of a changing E/I ratio and the tuning of total synaptic input. Furthermore, they provide experimental support in awake animals for the "Stabilized Supralinear Network," a model that explains diverse cortical phenomena, and suggest that a decreasing E/I ratio with increasing cortical drive could contribute to many different cortical computations.

Nonuniform surround suppression of visual responses in mouse V1

Complex receptive field characteristics, distributed across a population of neurons, are thought to be critical for solving perceptual inference problems that arise during motion and image segmentation. For example, in a class of neurons referred to as "end-stopped," increasing the length of stimuli outside of the bar-responsive region into the surround suppresses responsiveness. It is unknown whether these properties exist for receptive field surrounds in the mouse. We examined surround modulation in layer 2/3 neurons of the primary visual cortex in mice using two-photon calcium imaging. We found that surround suppression was significantly asymmetric in 17% of the visually responsive neurons examined. Furthermore, the magnitude of asymmetry was correlated with orientation selectivity. Our results demonstrate that neurons in mouse primary visual cortex are differentially sensitive to the addition of elements in the surround and that individual neurons can be described as being either uniformly suppressed by the surround, end-stopped, or side-stopped. NEW & NOTEWORTHY Perception of visual scenes requires active integration of both local and global features to successfully segment objects from the background. Although the underlying circuitry and development of perceptual inference is not well understood, converging evidence indicates that asymmetry and diversity in surround modulation are likely fundamental for these computations. We determined that these key features are present in the mouse. Our results support the mouse as a model to explore the neural basis and development of surround modulation as it relates to perceptual inference.

Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials

Advanced statistical methods have enabled trial-by-trial inference of the underlying excitatory and inhibitory synaptic conductances (SCs) of membrane-potential recordings. Simultaneous inference of both excitatory and inhibitory SCs sheds light on the neural circuits underlying the neural activity and advances our understanding of neural information processing. Conventional Bayesian methods can infer excitatory and inhibitory SCs based on a single trial of observed membrane potential. However, if multiple recorded trials are available, this typically leads to suboptimal estimation because they neglect common statistics (of synaptic inputs (SIs)) across trials. Here, we establish a new expectation maximization (EM) algorithm that improves such single-trial Bayesian methods by exploiting multiple recorded trials to extract common SI statistics across the trials. In this paper, the proposed EM algorithm is embedded in parallel Kalman filters or particle filters for multiple recorded trials to integrate their outputs to iteratively update the common SI statistics. These statistics are then used to infer the excitatory and inhibitory SCs of individual trials. We demonstrate the superior performance of multiple-trial Kalman filtering (MtKF) and particle filtering (MtPF) relative to that of the corresponding single-trial methods. While relative estimation error of excitatory and inhibitory SCs is known to depend on the level of current injection into a cell, our numerical simulations using MtKF show that both excitatory and inhibitory SCs are reliably inferred using an optimal level of current injection. Finally, we validate the robustness and applicability of our technique through simulation studies, and we apply MtKF to in vivo data recorded from the rat barrel cortex.

Phasic, suprathreshold excitation and sustained inhibition underlie neuronal selectivity for short-duration sounds

Sound duration is important in acoustic communication, including speech recognition in humans. Although duration-selective auditory neurons have been found, the underlying mechanisms are unclear. To investigate these mechanisms we combined in vivo whole-cell patch recordings from midbrain neurons, extraction of excitatory and inhibitory conductances, and focal pharmacological manipulations. We show that selectivity for short-duration stimuli results from integration of short-latency, sustained inhibition with delayed, phasic excitation; active membrane properties appeared to amplify responses to effective stimuli. Blocking GABAA receptors attenuated stimulus-related inhibition, revealed suprathreshold excitation at all stimulus durations, and decreased short-pass selectivity without changing resting potentials. Blocking AMPA and NMDA receptors to attenuate excitation confirmed that inhibition tracks stimulus duration and revealed no evidence of postinhibitory rebound depolarization inherent to coincidence models of duration selectivity. These results strongly support an anticoincidence mechanism of short-pass selectivity, wherein inhibition and suprathreshold excitation show greatest temporal overlap for long duration stimuli.

Canonical computations of cerebral cortex

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

The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex

Neurons in sensory cortex integrate multiple influences to parse objects and support perception. Across multiple cortical areas, integration is characterized by two neuronal response properties: (1) surround suppression--modulatory contextual stimuli suppress responses to driving stimuli; and (2) "normalization"--responses to multiple driving stimuli add sublinearly. These depend on input strength: for weak driving stimuli, contextual influences facilitate or more weakly suppress and summation becomes linear or supralinear. Understanding the circuit operations underlying integration is critical to understanding cortical function and disease. We present a simple, general theory. A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; and (4) simple spatial properties of intracortical connections. Integrative properties emerge dynamically as circuit properties, with excitatory and inhibitory neurons showing similar behaviors. In new recordings in visual cortex, we confirm key model predictions.

Surround suppression and temporal processing of visual signals

Extraclassical surround suppression strongly modulates responses of neurons in the retina, lateral geniculate nucleus (LGN), and primary visual cortex. Although a great deal is known about the spatial properties of extraclassical suppression and the role it serves in stimulus size tuning, relatively little is known about how extraclassical suppression shapes visual processing in the temporal domain. We recorded the spiking activity of retinal ganglion cells and LGN neurons in the cat to test the hypothesis that extraclassical suppression influences temporal features of visual responses in the early visual system. Our results demonstrate that extraclassical suppression not only shifts the distribution of interspike intervals in a manner that decreases the efficacy of neuronal communication, it also decreases the reliability of neuronal responses to visual stimuli and it decreases the duration of visual responses, an effect that underlies a rightward shift in the temporal frequency tuning of LGN neurons. Taken together, these results reveal a dynamic relationship between extraclassical suppression and the temporal features of neuronal responses.

Spatiotemporal characteristics of surround suppression in primary visual cortex and lateral geniculate nucleus of the cat

In the primary visual cortex (V1), a neuronal response to stimulation of the classical receptive field (CRF) is predominantly suppressed by a stimulus presented outside the CRF (extraclassical receptive field, ECRF), a phenomenon referred to as ECRF suppression. To elucidate the neuronal mechanisms and origin of ECRF suppression in V1 of anesthetized cats, we examined the temporal properties of the spatial extent and orientation specificity of ECRF suppression in V1 and the lateral geniculate nucleus (LGN), using stationary-flashed sinusoidal grating. In V1, we found three components of ECRF suppression: 1) local and fast, 2) global and fast, and 3) global and late. The local and fast component, which resulted from within 2° of the boundary of the CRF, started no more than 10 ms after the onset of the CRF response and exhibited low specificity for the orientation of the ECRF stimulus. These spatiotemporal properties corresponded to those of geniculate ECRF suppression, suggesting that the local and fast component of V1 is inherited from the LGN. In contrast, the two global components showed rather large spatial extents ∼5° from the CRF boundary and high specificity for orientation, suggesting that their possible origin is the cortex, not the LGN. Correspondingly, the local component was observed in all neurons of the thalamocortical recipient layer, while the global component was biased toward other layers. Therefore, we conclude that both subcortical and cortical mechanisms with different spatiotemporal properties are involved in ECRF suppression.

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

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

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

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

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

Relationship between size summation properties, contrast sensitivity and response latency in the dorsomedial and middle temporal areas of the primate extrastriate cortex

Analysis of the physiological properties of single neurons in visual cortex has demonstrated that both the extent of their receptive fields and the latency of their responses depend on stimulus contrast. Here, we explore the question of whether there are also systematic relationships between these response properties across different cells in a neuronal population. Single unit recordings were obtained from the middle temporal (MT) and dorsomedial (DM) extrastriate areas of anaesthetized marmoset monkeys. For each cell, spatial integration properties (length and width summation, as well as the presence of end- and side-inhibition within 15° of the receptive field centre) were determined using gratings of optimal direction of motion and spatial and temporal frequencies, at 60% contrast. Following this, contrast sensitivity was assessed using gratings of near-optimal length and width. In both areas, we found a relationship between spatial integration and contrast sensitivity properties: cells that summated over smaller areas of the visual field, and cells that displayed response inhibition at larger stimulus sizes, tended to show higher contrast sensitivity. In a sample of MT neurons, we found that cells showing longer latency responses also tended to summate over larger expanses of visual space in comparison with neurons that had shorter latencies. In addition, longer-latency neurons also tended to show less obvious surround inhibition. Interestingly, all of these effects were stronger and more consistent with respect to the selectivity for stimulus width and strength of side-inhibition than for length selectivity and end-inhibition. The results are partially consistent with a hierarchical model whereby more extensive receptive fields require convergence of information from larger pools of “feedforward” afferent neurons to reach near-optimal responses. They also suggest that a common gain normalization mechanism within MT and DM is involved, the spatial extent of which is more evident along the cell’s preferred axis of motion.

Surround modulation characteristics of local field potential and spiking activity in primary visual cortex of cat

In primary visual cortex, spiking activity that evoked by stimulus confined in receptive field can be modulated by surround stimulus. This center-surround interaction is hypothesized to be the basis of visual feature integration and segregation. Spiking output has been extensively reported to be surround suppressive. However, less is known about the modulation properties of the local field potential (LFP), which generally reflects synaptic inputs. We simultaneously recorded spiking activity and LFP in the area 17 of anesthetized cats to examine and compare their modulation characteristics. When the stimulus went beyond the classical receptive field, LFP exhibited decreased power along the gamma band (30–100 Hz) in most of our recording sites. Further investigation revealed that suppression of the LFP gamma mean power (gLFP) depended on the angle between the center and surround orientations. The strongest suppression was induced when center and surround orientations were parallel. Moreover, the surround influence of the gLFP exhibited an asymmetric spatial organization. These results demonstrate that the gLFP has similar but not identical surround modulation properties, as compared to the spiking activity. The spatiotemporal integration of LFP implies that the oscillation and synchronization of local synaptic inputs may have important functions in surround modulation.

Analysis of the stabilized supralinear network

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

Fovea-periphery axis symmetry of surround modulation in the human visual system

A visual stimulus activates different sized cortical area depending on eccentricity of the stimulus. Here, our aim is to understand whether the visual field size of a stimulus or cortical size of the corresponding representation determines how strongly it interacts with other stimuli. We measured surround modulation of blood-oxygenation-level-dependent signal and perceived contrast with surrounds that extended either towards the periphery or the fovea from a center stimulus, centered at 6° eccentricity. This design compares the effects of two surrounds which are identical in visual field size, but differ in the sizes of their cortical representations. The surrounds produced equally strong suppression, which suggests that visual field size of the surround determines suppression strength. A modeled population of neuronal responses, in which all the parameters were experimentally fixed, captured the pattern of results both in psychophysics and functional magnetic resonance imaging. Although the fovea-periphery anisotropy affects nearly all aspects of spatial vision, our results suggest that in surround modulation the visual system compensates for it.

Contrast-dependent variations in the excitatory classical receptive field and suppressive nonclassical receptive field of cat primary visual cortex

In area V1 of cat and monkey, there is a surround region beyond the classical receptive field (CRF) which alone is unresponsive but may modulate the cell's response. This field is referred to as the "nonclassical receptive field" (nCRF). It has been reported in monkey that the extent of CRF and/or nCRF of V1 neurons is not fixed but varies with stimulus contrast. We reexamined the contrast dependence of V1 neurons in cat to determine whether this differs from previous studies in macaque. By fitting the spatial summation curves obtained at different contrasts with a difference of Gaussians model, we estimated quantitatively the effect of contrast on the spatial extent of the CRF and nCRF as well as the strength of surround suppression. Our results showed that both the CRF and nCRF expanded at low contrast, but the expansion is more marked for the CRF than for the nCRF. Although the effect of contrast on surround suppression was varied, the overall suppression increased significantly at high contrast. Moreover, the contrast-dependent change in the extent of CRF is independent of the change in suppression strength. Overall, our results in cat are in agreement with those obtained in macaque money.

Contrast sensitivity of MT receptive field centers and surrounds

Neurons throughout the visual system have receptive fields with both excitatory and suppressive components. The latter are responsible for a phenomenon known as surround suppression, in which responses decrease as a stimulus is extended beyond a certain size. Previous work has shown that surround suppression in the primary visual cortex depends strongly on stimulus contrast. Such complex center-surround interactions are thought to relate to a variety of functions, although little is known about how they affect responses in the extrastriate visual cortex. We have therefore examined the interaction of center and surround in the middle temporal (MT) area of the macaque ( Macaca mulatta) extrastriate cortex by recording neuronal responses to stimuli of different sizes and contrasts. Our findings indicate that surround suppression in MT is highly contrast dependent, with the strongest suppression emerging unexpectedly at intermediate stimulus contrasts. These results can be explained by a simple model that takes into account the nonlinear contrast sensitivity of the neurons that provide input to MT. The model also provides a qualitative link to previous reports of a topographic organization of area MT based on clusters of neurons with differing surround suppression strength. We show that this organization can be detected in the gamma-band local field potentials (LFPs) and that the model parameters can predict the contrast sensitivity of these LFP responses. Overall our results show that surround suppression in area MT is far more common than previously suspected, highlighting the potential functional importance of the accumulation of nonlinearities along the dorsal visual pathway.

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.

Modulation of firing rate by background synaptic noise statistics in rat visual cortical neurons

It has been shown previously that background synaptic noise modulates the response gain of neocortical neurons. However, the role of the statistical properties of the noise in modulating firing rate is not known. Here, the dependence of firing rate on the statistical properties of the excitatory to inhibitory balance (EI) in cortical pyramidal neurons was studied. Excitatory glutamatergic and inhibitory GABAergic synaptic conductances were simulated as two stochastic processes and injected into individual neurons in vitro through use of the dynamic-clamp system. Response gain was significantly modulated as a function of the statistical interactions between excitatory and inhibitory synaptic conductances. Firing rates were compared for noisy synaptic conductance steps by varying either the EI correlation or the relative delay between correlated E and I. When inhibitory synaptic conductances exhibited a short temporal delay (5 ms) relative to correlated excitatory synaptic conductances, the response gain was increased compared with noise with no temporal delay but with an equivalent degree of correlation. The dependence of neuronal firing rate on the EI delay of the noisy background synaptic conductance suggests that individual excitatory pyramidal neurons are sensitive to the EI balance of the synaptic conductance. Therefore the statistical EI interactions encoded within the synaptic subthreshold membrane fluctuations are able to modulate neuronal firing properties.

Stimulus-dependent suppression of chaos in recurrent neural networks

Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a "resonant" frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.

Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation

During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RS(C)) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RS(C) neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses.

Area summation in human visual system: psychophysics, fMRI, and modeling

Contextual modulation is a fundamental feature of sensory processing, both on perceptual and on single-neuron level. When the diameter of a visual stimulus is increased, the firing rate of a cell typically first increases (summation field) and then decreases (surround field). Such an area summation function draws a comprehensive profile of the receptive field structure of a neuron, including areas outside the classical receptive field. We investigated area summation in human vision with psychophysics and functional magnetic resonance imaging (fMRI). The stimuli were drifting sine wave gratings similar to those used in previous macaque single-cell area summation studies [corrected]. A model was developed to facilitate comparison of area summation in fMRI to area summation in psychophysics and single cells. The model consisted of units with an antagonistic receptive field structure found in single cells in the primary visual cortex. The receptive field centers of the model neurons were distributed in the region of the visual field covered by a single voxel. The measured area summation functions were qualitatively similar to earlier single-cell data. The model with parameters derived from psychophysics captured the spatial structure of the summation field in the primary visual cortex as measured with fMRI. The model also generalized to a novel situation in which the neural population was displaced from the stimulus center. The current study shows that contextual modulation arises from similar spatially antagonistic and overlapping excitatory and inhibitory mechanisms, both in single cells and in human vision.

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.

Feature detection and the hypercomplex property in insects

Discerning a target amongst visual 'clutter' is a complicated task that has been elegantly solved by flying insects, as evidenced by their mid-air interactions with conspecifics and prey. The neurophysiology of small-target motion detectors (STMDs) underlying these complex behaviors has recently been described and suggests that insects use mechanisms similar to those of hypercomplex cells of the mammalian visual cortex to achieve target-specific tuning. Cortical hypercomplex cells are end-stopped, which means that they respond optimally to small moving targets, with responses to extended bars attenuated. We review not only the underlying mechanisms involved in this tuning but also how recently proposed models provide a possible explanation for another remarkable property of these neurons - their ability to respond robustly to the motion of targets even against moving backgrounds.

Inhibitory stabilization of the cortical network underlies visual surround suppression

In what regime does the cortical circuit operate? Our intracellular studies of surround suppression in cat primary visual cortex (V1) provide strong evidence on this question. Although suppression has been thought to arise from an increase in lateral inhibition, we find that the inhibition that cells receive is reduced, not increased, by a surround stimulus. Instead, suppression is mediated by a withdrawal of excitation. Thalamic recordings and previous work show that these effects cannot be explained by a withdrawal of thalamic input. We find in theoretical work that this behavior can only arise if V1 operates as an inhibition-stabilized network (ISN), in which excitatory recurrence alone is strong enough to destabilize visual responses but feedback inhibition maintains stability. We confirm two strong tests of this scenario experimentally and show through simulation that observed cell-to-cell variability in surround effects, from facilitation to suppression, can arise naturally from variability in the ISN.

Surround suppression sharpens orientation tuning in the cat primary visual cortex

In the primary visual cortex (V1), the response of a neuron to stimulation of its classical receptive field (CRF) is suppressed by concurrent stimulation of the extraclassical receptive field (ECRF), a phenomenon termed 'surround suppression'. It is also known that the orientation tuning of V1 neurons becomes sharper as the size of the stimulus increases beyond the CRF. However, there have been few quantitative investigations of the relationship between sharpening of orientation tuning and surround suppression. We examined this relationship in 73 V1 neurons recorded from anesthetized and paralysed cats using sinusoidal grating patches as stimuli. We found that sharpening of orientation tuning was significantly correlated with the strength of surround suppression for large stimuli that cover both CRF and ECRF. Furthermore, simulation analysis using a variety of tuning widths and most suppressive orientation of orientation-tuned surround suppression demonstrated that broadly orientation-tuned surround suppression sharpens orientation tuning for large gratings without shift in optimal orientation. Our findings suggest that one of the functional roles of surround suppression in V1 is enhancement of orientation discrimination for large and uniformly patterned objects.

Super resolution: another computational role of short-range horizontal connection in the primary visual cortex

Recent physiological data related to the primary visual cortex (V1) have shown various contextual effects in the non-classical receptive field (nCRF). Contextual modulation, size tuning and altered sensitivity of orientation are typical examples of such contextual effects in the nCRF. These phenomena in the nCRF have been thought to be caused by short-range horizontal connection (SHC). However, SHC does not necessarily contribute only to these phenomena. These phenomena might be merely secondary phenomena by the fundamental role of SHC. In this paper, we specifically address the overcomplete properties in V1. Then the fundamental role of SHC is examined from image-processing points of view. Super resolution is proposed as a strong candidate for the fundamental role of SHC. Super resolution is an engineering method that obtains a high-resolution image from a low-resolution image(s). The distribution of SHC is deductively derived by adopting a reverse diffusion technique, which is one of various available super-resolution techniques. The spatial distribution of our proposed SHC is isotropic on the orientation map. This characteristic is consistent with physiological data. In addition to that, contextual modulation, size tuning and altered sensitivity of orientation in numerical experiments using our proposed SHC can be reproduced qualitatively. These results indicate that these phenomena are secondary phenomena by super-resolution processing.

Gain modulation of neuronal responses by subtractive and divisive mechanisms of inhibition

Gain modulation of neuronal responses is widely observed in the cerebral cortex of both anesthetized and behaving animals. Does this multiplicative effect on neuronal tuning curves require underlying multiplicative mechanisms of integration? We compare the effects of a divisive mechanism of inhibition (noisy excitatory and inhibitory synaptic inputs) with the effects of two subtractive mechanisms (shunting conductance and hyperpolarizing current) on the tuning curves of a model cortical neuron. We find that, although the effects of subtractive inhibition can appear nonlinear, they are accompanied by a change in response threshold and are best described as a vertical shift along the response axis. Increasing noisy synaptic activity divisively scales the model responses, reproducing a response-gain control effect. When mutual inhibition between subpopulations of local neurons is included, the model exhibits a gain modulation effect that is better described as input-gain control. We apply these findings to experimental data by examining how noisy synaptic input may underlie divisive surround suppression and attention-driven gain modulation of neuronal responses in the visual system.

Occipital network for figure/ground organization

To study the cortical mechanism of figure/ground categorization in the human brain, we employed fMRI and the temporal-asynchrony paradigm. This paradigm is able to eliminate any differential activation for local stimulus features, and thus to identify only global perceptual interactions. Strong segmentation of the image into different spatial configurations was generated solely from temporal asynchronies between zones of homogeneous dynamic noise. The figure/ground configuration was a single geometric figure enclosed in a larger surround region. In a control condition, the figure/ground organization was eliminated by segmenting the noise field into many identical temporal-asynchrony stripes. The manipulation of the type of perceptual organization triggered dramatic reorganization in the cortical activation pattern. The figure/ground configuration generated suppression of the ground representation (limited to early retinotopic visual cortex, V1 and V2) and strong activation in the motion complex hMT+/V5+; conversely, both responses were abolished when the figure/ground organization was eliminated. These results suggest that figure/ground processing is mediated by top-down suppression of the ground representation in the earliest visual areas V1/V2 through a signal arising in the motion complex. We propose a model of a recurrent cortical architecture incorporating suppressive feedback that operates in a topographic manner, forming a figure/ground categorization network distinct from that for "pure" scene segmentation and thus underlying the perceptual organization of dynamic scenes into cognitively relevant components.

Inhibition, spike threshold, and stimulus selectivity in primary visual cortex

Ever since Hubel and Wiesel described orientation selectivity in the visual cortex, the question of how precise selectivity emerges has been marked by considerable debate. There are essentially two views of how selectivity arises. Feed-forward models rely entirely on the organization of thalamocortical inputs. Feedback models rely on lateral inhibition to refine selectivity relative to a weak bias provided by thalamocortical inputs. The debate is driven by two divergent lines of evidence. On the one hand, many response properties appear to require lateral inhibition, including precise orientation and direction selectivity and crossorientation suppression. On the other hand, intracellular recordings have failed to find consistent evidence for lateral inhibition. Here we demonstrate a resolution to this paradox. Feed-forward models incorporating the intrinsic nonlinear properties of cortical neurons and feed-forward circuits (i.e., spike threshold, contrast saturation, and spike-rate rectification) can account for properties that have previously appeared to require lateral inhibition.

In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

In order to better understand the synaptic nature of the integration process operated by cortical neurons during sensory processing, it is necessary to devise quantitative methods which allow one to infer the level of conductance change evoked by the sensory stimulation and, consequently, the dynamics of the balance between excitation and inhibition. Such detailed measurements are required to characterize the static versus dynamic nature of the non-linear interactions triggered at the single cell level by sensory stimulus. This paper primarily reviews experimental data from our laboratory based on direct conductance measurements during whole-cell patch clamp recordings in two experimental preparations: (1) in vitro, during electrical stimulation in the visual cortex of the rat and (2) in vivo, during visual stimulation, in the primary visual cortex of the anaesthetized cat. Both studies demonstrate that shunting inhibition is expressed as well in vivo as in vitro. Our in vivo data reveals that a high level of diversity is observed in the degree of interaction (from linear to non-linear) and in the temporal interplay (from push-pull to synchronous) between stimulus-driven excitation (E) and inhibition (I). A detailed analysis of the E/I balance during evoked spike activity further shows that the firing strength results from a simultaneous decrease of evoked inhibition and increase of excitation. Secondary, the paper overviews the various computational methods used in the literature to assess conductance dynamics, measured in current clamp as well as in voltage clamp in different neocortical areas and species, and discuss the consistency of their estimations.

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.

The unsolved mystery of vision

Vision looms large in neuroscience--it is the subject of a gigantic literature and four Nobel prizes--but there is a growing realization that there are problems with the textbook explanation of how mammalian vision works. Here we will summarize the evidence behind this disquiet. In effect, we shall present a portrait of a field that is 'stuck'. Our initial focus, because it is our area of expertise, is on evidence that the early steps of mammalian vision are more diverse and more interesting than is usually imagined, so that our understanding of the later stages is in trouble right from the start. But we will also summarize problems, raised by others, with the later stages themselves.

Cue-invariant detection of centre-surround discontinuity by V1 neurons in awake macaque monkey

Visual perception of an object depends on the discontinuity between the object and its background, which can be defined by a variety of visual features, such as luminance, colour and motion. While human object perception is largely cue invariant, the extent to which neural mechanisms in the primary visual cortex contribute to cue-invariant perception has not been examined extensively. Here we report that many V1 neurons in the awake monkey are sensitive to the stimulus discontinuity between their classical receptive field (CRF) and non-classical receptive field (nCRF) regardless of the visual feature that defines the discontinuity. The magnitude of this sensitivity is strongly dependent on the strength of nCRF suppression of the cell. These properties of V1 neurons may contribute significantly to cue-invariant object perception.

Visual neuroscience: hypercomplex cells in the arthropod visual system

Newly described visual interneurons in flies have sophisticated receptive field properties reminiscent of neurons in the mammalian visual cortex. The cells are well-suited to compute motion of conspecific females that male flies aerially intercept.

Unbalanced synaptic inhibition can create intensity-tuned auditory cortex neurons

Intensity-tuned auditory cortex neurons have spike rates that are nonmonotonic functions of sound intensity: their spike rate initially increases and peaks as sound intensity is increased, then decreases as sound intensity is further increased. They are either "unbalanced," receiving disproportionally large synaptic inhibition at high sound intensities; or "balanced," receiving intensity-tuned synaptic excitation and identically tuned synaptic inhibition which neither creates enhances nor creates intensity-tuning. It has remained unknown if the synaptic inhibition received by unbalanced neurons enhances intensity-tuning already present in the synaptic excitation, or if it creates intensity-tuning that is not present in the synaptic excitation. Here we show, using in vivo whole cell recordings in pentobarbital-anesthetized rats, that in some unbalanced intensity-tuned auditory cortex neurons synaptic inhibition enhances the intensity-tuning; while in others it actually creates the intensity-tuning. The lack of balance between synaptic excitation and inhibition was not always apparent in their peak amplitudes, but could sometimes be revealed only by considering their relative timing. Since synaptic inhibition is essentially cortical in origin, the unbalanced neurons in which inhibition creates intensity-tuning provide examples of auditory feature-selectivity arising de novo at the auditory cortex.

Estimation of synaptic conductances

In order to identify and understand mechanistically the cortical circuitry of sensory information processing estimates are needed of synaptic input fields that drive neurons. From intracellular in vivo recordings one would like to estimate net synaptic conductance time courses for excitation and inhibition, g(E)(t) and g(I)(t), during time-varying stimulus presentations. However, the intrinsic conductance transients associated with neuronal spiking can confound such estimates, and thereby jeopardize functional interpretations. Here, using a conductance-based pyramidal neuron model we illustrate errors in estimates when the influence of spike-generating conductances are not reduced or avoided. A typical estimation procedure involves approximating the current-voltage relation at each time point during repeated stimuli. The repeated presentations are done in a few sets, each with a different steady bias current. From the trial-averaged smoothed membrane potential one estimates total membrane conductance and then dissects out estimates for g(E)(t) and g(I)(t). Simulations show that estimates obtained during phases without spikes are good but those obtained from phases with spiking should be viewed with skeptism. For the simulations, we consider two different synaptic input scenarios, each corresponding to computational network models of orientation tuning in visual cortex. One input scenario mimics a push-pull arrangement for g(E)(t) and g(I)(t) and idealized as specified smooth time courses. The other is taken directly from a large-scale network simulation of stochastically spiking neurons in a slab of cortex with recurrent excitation and inhibition. For both, we show that spike-generating conductances cause serious errors in the estimates of g(E) and g(I). In some phases for the push-pull examples even the polarity of g(I) is mis-estimated, indicating significant increase when g(I) is actually decreased. Our primary message is to be cautious about forming interpretations based on estimates developed during spiking phases.

'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.

End stopping in V1 is sensitive to contrast

Common situations that result in different perceptions of grouping and border ownership, such as shadows and occlusion, have distinct sign-of-contrast relationships at their edge-crossing junctions. Here we report a property of end stopping in V1 that distinguishes among different sign-of-contrast situations, thereby obviating the need for explicit junction detectors. We show that the inhibitory effect of the end zones in end-stopped cells is highly selective for the relative sign of contrast between the central activating stimulus and stimuli presented at the end zones. Conversely, the facilitatory effect of end zones in length-summing cells is not selective for the relative sign of contrast between the central activating stimulus and stimuli presented at the end zones. This finding indicates that end stopping belongs in the category of cortical computations that are selective for sign of contrast, such as direction selectivity and disparity selectivity, but length summation does not.

Contrast-dependent, contextual response modulation in primary visual cortex and lateral geniculate nucleus of the cat

In the primary visual cortex (V1), the responses of neurons to stimuli presented in their classical receptive fields (CRFs) are modulated by another stimulus concurrently presented in their surround (receptive field surround, SRF). We studied the nature of the modulatory effects of SRF stimulation with respect to stimulus contrast in cat V1. In 51 V1 neurons studied, large SRF stimuli (40 degreesx30 degrees ) induced only the suppression of responses to CRF stimulation and the suppressive effects became stronger as the contrast for SRF stimulation increased. The contrast sensitivity of SRF suppression did not correlate with that of CRF responses. By independently controlling contrast of CRF and SRF stimuli, we studied whether SRF effects vary with CRF response magnitude. Increasing contrast for CRF stimulation caused an upward shift of the range of effective contrasts for SRF stimulation, indicating that a high contrast for SRF stimulation is required for suppressing strong responses to CRF stimulation at high contrasts. To assess the possible origin of the suppressive SRF effect on V1 neurons, we also investigated the contrast dependency of SRF effects in 28 neurons from the lateral geniculate nucleus. Our results suggest that SRF effects obtained at the subcortical level strongly contribute to those in V1. Taken together, we conclude that along the thalamocortical projections, SRF modulation exhibits a gain-control mechanism that scales the suppressive SRF effect depending on the contrast for CRF stimulation. In addition, SRF effects can be facilitatory at low stimulus contrasts potentially due to the enlargement of the summation field.

Extraclassical Receptive Field Phenomena and Short-Range Connectivity in V1

Extraclassical receptive field phenomena in V1 are commonly attributed to long-range lateral connections and/or extrastriate feedback. We address 2 such phenomena: surround suppression and receptive field expansion at low contrast. We present rigorous computational support for the hypothesis that the phenomena largely result from local short-range (< 0.5 mm) cortical connections and lateral geniculate nucleus input. The neural mechanisms of surround suppression in our simulations operate via (A) enhancement of inhibition, (B) reduction of excitation, or (C) action of both simultaneously. Mechanisms (B) and (C) are substantially more prevalent than (A). We observe, on average, a growth in the spatial summation extent of excitatory and inhibitory synaptic inputs for low-contrast stimuli. However, we find this is neither sufficient nor necessary to explain receptive field expansion at low contrast, which usually involves additional changes in the relative gain of these inputs.

End-stopping in the visual cortex: excitation or inhibition?

In the visual cortex some neurons respond more strongly to short stimuli than to long ones. This is referred to as "end-stopping" and has been generally attributed to inhibition. The role of inhibition, however, has been difficult to demonstrate. Moreover, modeling has shown that end-stopping can be created solely from excitation. The roles of excitation and inhibition were investigated using intracellular recordings (Anderson et al., 2001, J. Neurosci. 21: 2104-2112). The results of that study were interpreted in favor of inhibition. The present report re-examines these results and finds that they may be in good, perhaps even better, agreement with an excitation model of end-stopping.

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.