Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons?

It is commonly assumed that the orientation-selective surround field of neurons in primary visual cortex (V1) is due to interactions provided solely by intrinsic long-range horizontal connections. We review evidence for and against this proposition and conclude that horizontal connections are too slow and cover too little visual field to subserve all the functions of suppressive surrounds of V1 neurons in the macaque monkey. We show that the extent of visual space covered by horizontal connections corresponds to the region of low contrast summation of the receptive field center mechanism. This region encompasses the classically defined receptive field center and the proximal surround. Beyond this region, feedback connections are the most likely substrate for surround suppression. We present evidence that inactivation of higher order areas leads to a major decrease in the strength of the suppressive surround of neurons in lower order areas, supporting the hypothesis that feedback connections play a major role in center-surround interactions.     

Origin and dynamics of extraclassical suppression in the lateral geniculate nucleus of the macaque monkey

In addition to the classical, center/surround receptive field of neurons in the lateral geniculate nucleus (LGN), there is an extraclassical, nonlinear surround that can strongly suppress LGN responses. This form of suppression likely plays an important role in adjusting the gain of LGN responses to visual stimuli. We performed experiments in alert and anesthetized macaque monkies to quantify extraclassical suppression in the LGN and determine the roles of feedforward and feedback pathways in the generation of LGN suppression. Results show that suppression is significantly stronger among magnocellular neurons than parvocellular neurons and that suppression arises too quickly for involvement from cortical feedback. Furthermore, the amount of suppression supplied by the retina is not significantly different from that in the LGN. These results indicate that extraclassical suppression in the macaque LGN relies on feedforward mechanisms and suggest that suppression in the cortex likely includes a component established in the retina.     

Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

We study the orientation and speed tuning properties of spatiotemporal three-dimensional (3D) Gabor and motion energy filters as models of time-dependent receptive fields of simple and complex cells in the primary visual cortex (V1). We augment the motion energy operator with surround suppression to model the inhibitory effect of stimuli outside the classical receptive field. We show that spatiotemporal integration and surround suppression lead to substantial noise reduction. We propose an effective and straightforward motion detection computation that uses the population code of a set of motion energy filters tuned to different velocities. We also show that surround inhibition leads to suppression of texture and thus improves the visibility of object contours and facilitates figure/ground segregation and the detection and recognition of objects.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.     

Feedback contributions to visual awareness in human occipital cortex

It has traditionally been assumed that processing within the visual system proceeds in a bottom-up, feedforward manner from retina to higher cortical areas. In addition to feedforward processing, it is now clear that there are also important contributions to sensory encoding that rely upon top-down, feedback (reentrant) projections from higher visual areas to lower ones. By utilizing transcranial magnetic stimulation (TMS) in a metacontrast masking paradigm, we addressed whether feedback processes in early visual cortex play a role in visual awareness. We show that TMS of visual cortex, when timed to produce visual suppression of an annulus serving as a metacontrast mask, induces recovery of an otherwise imperceptible disk. In addition to producing disk recovery, TMS suppression of an annulus was greater when a disk preceded it than when an annulus was presented alone. This latter result suggests that there are effects of the disk on the perceptibility of the subsequent mask that are additive and are revealed with TMS of the visual cortex. These results demonstrate spatial and temporal interactions of conscious vision in visual cortex and suggest that a prior visual stimulus can influence subsequent perception at early stages of visual encoding via feedback projections.     

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience.     

Spike suppression in a local cortical circuit induced by transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) noninvasively interferes with human cortical function, and is widely used as an effective technique for probing causal links between neural activity and cognitive function. However, the physiological mechanisms underlying TMS-induced effects on neural activity remain unclear. We examined the mechanism by which TMS disrupts neural activity in a local circuit in early visual cortex using a computational model consisting of conductance-based spiking neurons with excitatory and inhibitory synaptic connections. We found that single-pulse TMS suppressed spiking activity in a local circuit model, disrupting the population response. Spike suppression was observed when TMS was applied to the local circuit within a limited time window after the local circuit received sensory afferent input, as observed in experiments investigating suppression of visual perception with TMS targeting early visual cortex. Quantitative analyses revealed that the magnitude of suppression was significantly larger for synaptically-connected neurons than for isolated individual neurons, suggesting that intracortical inhibitory synaptic coupling also plays an important role in TMS-induced suppression. A conventional local circuit model of early visual cortex explained only the early period of visual suppression observed in experiments. However, models either involving strong recurrent excitatory synaptic connections or sustained excitatory input were able to reproduce the late period of visual suppression. These results suggest that TMS targeting early visual cortex disrupts functionally distinct neural signals, possibly corresponding to feedforward and recurrent information processing, by imposing inhibitory effects through intracortical inhibitory synaptic connections.     

Development of spatial coarse-to-fine processing in the visual pathway

The sequential analysis of information in a coarse-to-fine manner is a fundamental mode of processing in the visual pathway. Spatial frequency (SF) tuning, arguably the most fundamental feature of spatial vision, provides particular intuition within the coarse-to-fine framework: low spatial frequencies convey global information about an image (e.g., general orientation), while high spatial frequencies carry more detailed information (e.g., edges). In this paper, we study the development of cortical spatial frequency tuning. As feedforward input from the lateral geniculate nucleus (LGN) has been shown to have significant influence on cortical coarse-to-fine processing, we present a firing-rate based thalamocortical model which includes both feedforward and feedback components. We analyze the relationship between various model parameters (including cortical feedback strength) and responses. We confirm the importance of the antagonistic relationship between the center and surround responses in thalamic relay cell receptive fields (RFs), and further characterize how specific structural LGN RF parameters affect cortical coarse-to-fine processing. Our results also indicate that the effect of cortical feedback on spatial frequency tuning is age-dependent: in particular, cortical feedback more strongly affects coarse-to-fine processing in kittens than in adults. We use our results to propose an experimentally testable hypothesis for the function of the extensive feedback in the corticothalamic circuit.     

Sensorimotor mismatch signals in primary visual cortex of the behaving mouse

Studies in anesthetized animals have suggested that activity in early visual cortex is mainly driven by visual input and is well described by a feedforward processing hierarchy. However, evidence from experiments on awake animals has shown that both eye movements and behavioral state can strongly modulate responses of neurons in visual cortex; the functional significance of this modulation, however, remains elusive. Using visual-flow feedback manipulations during locomotion in a virtual reality environment, we found that responses in layer 2/3 of mouse primary visual cortex are strongly driven by locomotion and by mismatch between actual and expected visual feedback. These data suggest that processing in visual cortex may be based on predictive coding strategies that use motor-related and visual input to detect mismatches between predicted and actual visual feedback.     

The influence of the corticothalamic projection on responses in thalamus and cortex

We review results on the in vivo properties of neurons in the dorsal lateral geniculate nucleus (dLGN) that receives its afferent input from the retina and projects to the visual cortex. In addition, the dLGN receives input from the brain stem and from a rather strong corticothalamic back-projection, which originates in layer 6 of the visual cortex. We compare the behaviour of dLGN cells during spontaneous changes of the frequency contents of the electroencephalograph (EEG) (which are mainly related to a changing brain stem influence), with those that are obtained when experimentally silencing the corticothalamic feedback. The spatial and temporal response properties of dLGN cells are compared during these two conditions, and we report that the neurons behave similarly during a synchronized EEG state and during inactive corticothalamic feedback. In both situations, dLGN cells are rather phasic and their remaining tonic activity is temporally dispersed, indicating a hyperpolarizing effect. By means of a novel method, we were able to chronically eliminate a large proportion of the corticothalamic projection neurons from the otherwise intact cortex. In this condition, we found that cortical cells also lose their EEG specific response differences but, in this instance, probably due to a facilitatory (depolarizing) plasticity reaction of the remaining network.     

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.     

Feedback Inhibition and Throughput Properties of an Integrate-and-Fire-or-Burst Network Model of Retinogeniculate Transmission

Computational modeling has played an important role in the dissection of the biophysical basis of rhythmic oscillations in thalamus that are associated with sleep and certain forms of epilepsy. In contrast, the dynamic filter properties of thalamic relay nuclei during states of arousal are not well understood. Here we present a modeling and simulation study of the throughput properties of the visually driven dorsal lateral geniculate nucleus (dLGN) in the presence of feedback inhibition from the perigeniculate nucleus (PGN). We employ thalamocortical (TC) and thalamic reticular (RE) versions of a minimal integrate-and-fire-or-burst type model and a one-dimensional, two-layered network architecture. Potassium leakage conductances control the neuromodulatory state of the network and eliminate rhythmic bursting in the presence of spontaneous input (i.e., wake up the network). The aroused dLGN/PGN network model is subsequently stimulated by spatially homogeneous spontaneous retinal input or spatio-temporally patterned input consistent with the activity of X-type retinal ganglion cells during full-field or drifting grating visual stimulation. The throughput properties of this visually-driven dLGN/PGN network model are characterized and quantified as a function of stimulus parameters such as contrast, temporal frequency, and spatial frequency. During low-frequency oscillatory full-field stimulation, feedback inhibition from RE neurons often leads to TC neuron burst responses, while at high frequency tonic responses dominate. Depending on the average rate of stimulation, contrast level, and temporal frequency of modulation, the TC and RE cell bursts may or may not be phase-locked to the visual stimulus. During drifting-grating stimulation, phase-locked bursts often occur for sufficiently high contrast so long as the spatial period of the grating is not small compared to the synaptic footprint length, i.e., the spatial scale of the network connectivity.     

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex

Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive fields with tunings for contrast, orientation, spatial frequency, and size, similar to those reported in the macaque. We also observed pronounced gamma oscillations in the local-field potential that could be used to estimate the underlying spiking response properties. Spiking responses were modulated by visual context and attention. We observed orientation-tuned surround suppression: responses were suppressed by image regions with a uniform orientation and enhanced by orientation contrast. Additionally, responses were enhanced on regions that perceptually segregated from the background, indicating that neurons in the human visual cortex are sensitive to figure-ground structure. Spiking responses were also modulated by object-based attention. When the patient mentally traced a curve through the neurons’ receptive fields, the accompanying shift of attention enhanced neuronal activity. These results demonstrate that the tuning properties of cells in the human early visual cortex are similar to those in the macaque and that responses can be modulated by both contextual factors and behavioral relevance. Our results, therefore, imply that the macaque visual system is an excellent model for the human visual cortex.     

Feedback enhances feedforward figure-ground segmentation by changing firing mode

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (～9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.     

S cone contributions to the magnocellular visual pathway in macaque monkey

The magnocellular visual pathway is believed to receive input from long (L) and middle (M), but not short (S), wavelength-sensitive cones. Recording from neurons in magnocellular layers of lateral geniculate nucleus (LGN) in macaque monkeys, we found that magnocellular neurons were unequivocally responsive to S cone-isolating stimuli. A quantitative analysis suggests that S cones provided about 10% of the input to these cells, on average, while L:M ratios were far more variable. S cone signals influenced responses with the same sign as L and M cone inputs (i.e., no color opponency). Magnocellular afferent recordings following inactivation of primary visual cortex demonstrated that S cone signals were feedforward in nature and did not arise from cortical feedback to LGN     

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed “input–output channels of communication” corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.     

Recurrent V1–V2 interaction in early visual boundary processing

A majority of cortical areas are connected via feedforward and feedback fiber projections. In feedforward pathways we mainly observe stages of feature detection and integration. The computational role of the descending pathways at different stages of processing remains mainly unknown. Based on empirical findings we suggest that the top-down feedback pathways subserve a context-dependent gain control mechanism. We propose a new computational model for recurrent contour processing in which normalized activities of orientation selective contrast cells are fed forward to the next processing stage. There, the arrangement of input activation is matched against local patterns of contour shape. The resulting activities are subsequently fed back to the previous stage to locally enhance those initial measurements that are consistent with the top-down generated responses. In all, we suggest a computational theory for recurrent processing in the visual cortex in which the significance of local measurements is evaluated on the basis of a broader visual context that is represented in terms of contour code patterns. The model serves as a framework to link physiological with perceptual data gathered in psychophysical experiments. It handles a variety of perceptual phenomena, such as the local grouping of fragmented shape outline, texture surround and density effects, and the interpolation of illusory contours. Received: 28 October 1998 / Accepted in revised form: 19 March 1999     

精神分裂症视觉周边抑制异常及其神经机制

精神分裂症患者普遍存在视觉信息处理异常,这些视知觉功能紊乱涉及视通路的高级以及低级视区,表明在部分精神分裂症患者中,视觉系统早期或晚期的不同信息处理阶段均可能存在损伤.阐明这些感知觉信息处理紊乱的神经机制对理解精神分裂症神经病理生理学机制有重大意义.视觉周边抑制(surround suppression)是一种广泛存在的视觉现象,指在神经生理水平或视知觉水平上外周对中央视觉目标的抑制作用.精神分裂症的视觉周边抑制发生异常改变,然而其损伤状况并不完全一致,且其具体神经机制目前仍不清楚.本文以周边抑制为对象,从精神分裂症周边抑制改变状况及其神经机制两个层面简述了国内外精神分裂症视觉周边抑制的研究进展.未来研究方向需要系统全面地调查精神分裂症周边抑制损伤状况,综合脑科学研究技术共同探究精神分裂症患者周边抑制异常的具体神经环路.