Orientation-Cue Invariant Population Responses to Contrast-Modulated and Phase-Reversed Contour Stimuli in Macaque V1 and V2

Visual scenes can be readily decomposed into a variety of oriented components, the processing of which is vital for object segregation and recognition. In primate V1 and V2, most neurons have small spatio-temporal receptive fields responding selectively to oriented luminance contours (first order), while only a subgroup of neurons signal non-luminance defined contours (second order). So how is the orientation of second-order contours represented at the population level in macaque V1 and V2? Here we compared the population responses in macaque V1 and V2 to two types of second-order contour stimuli generated either by modulation of contrast or phase reversal with those to first-order contour stimuli. Using intrinsic signal optical imaging, we found that the orientation of second-order contour stimuli was represented invariantly in the orientation columns of both macaque V1 and V2. A physiologically constrained spatio-temporal energy model of V1 and V2 neuronal populations could reproduce all the recorded population responses. These findings suggest that, at the population level, the primate early visual system processes the orientation of second-order contours initially through a linear spatio-temporal filter mechanism. Our results of population responses to different second-order contour stimuli support the idea that the orientation maps in primate V1 and V2 can be described as a spatial-temporal energy map.     

Local and global motion preferences in descending neurons of the fly

For a moving animal, optic flow is an important source of information about its ego-motion. In flies, the processing of optic flow is performed by motion sensitive tangential cells in the lobula plate. Amongst them, cells of the vertical system (VS cells) have receptive fields with similarities to optic flows generated during rotations around different body axes. Their output signals are further processed by pre-motor descending neurons. Here, we investigate the local motion preferences of two descending neurons called descending neurons of the ocellar and vertical system (DNOVS1 and DNOVS2). Using an LED arena subtending 240° × 95° of visual space, we mapped the receptive fields of DNOVS1 and DNOVS2 as well as those of their presynaptic elements, i.e. VS cells 1–10 and V2. The receptive field of DNOVS1 can be predicted in detail from the receptive fields of those VS cells that are most strongly coupled to the cell. The receptive field of DNOVS2 is a combination of V2 and VS cells receptive fields. Predicting the global motion preferences from the receptive field revealed a linear spatial integration in DNOVS1 and a superlinear spatial integration in DNOVS2. In addition, the superlinear integration of V2 output is necessary for DNOVS2 to differentiate between a roll rotation and a lift translation of the fly.     

Parietal neurons encoding visual space in a head-frame of reference.

Neurons of the visual system are known to have receptive fields organized in retinotopic coordinates. We wanted to test whether visual neurons existed whose receptive fields were organized in spatial coordinates. Extracellular recordings from single cells were carried out in one area of the posterior parietal cortex (area V6) of a behaving macaque monkey. Among a great majority of retinotopically organized visual cells, neurons whose visual receptive field did not shift with gaze were also found. These cells responded to the visual stimulation of the same spatial position independently of the animal's direction of gaze, that is, their receptive field was anchored to an absolute spatial location. We suggest that these neurons directly encode visual space and are involved in programming visually-guided motor actions in space.     

Rabbit visual cortical neurons with simple and complex receptive fields

Receptive fields of neurons of the rabbit visual cortex selective for stimulus orientation were investigated. These receptive fields were less well differentiated than those of the analogous neurons of the cat visual cortex (large in size and circular in shape). Two mechanisms of selectivity for stimulus orientation were observed: inhibition between on and off zones of the receptive field (sample type) and oriented lateral inhibition within the same zone of the receptive field (complex type). Lateral inhibition within the same zone of the receptive field also took place in unselective neurons; "complex" selective neurons differed from them in the orientation of this inhibition. A combination of both mechanisms was possible in the receptive field of the same neuron. It is suggested that both simple and complex receptive fields are derivatives of unselective receptive fields and that "complex" neurons are not the basis for a higher level of analysis of visual information than in "simple" neurons.A. N. Severtsov Institute of Evolutionary Morphology and Ecology of Animals, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 10, No. 1, pp. 13–21, January–February, 1978.     

Receptive fields of disparity-tuned simple cells in macaque V1

Binocular simple cells in primary visual cortex (V1) are the first cells along the mammalian visual pathway to receive input from both eyes. Two models of how binocular simple cells could extract disparity information have been put forward. The phase-shift model proposes that the receptive fields in the two eyes have different subunit organizations, while the position-shift model proposes that they have different overall locations. In five fixating macaque monkeys, we recorded from 30 disparity-tuned simple cells that showed selectivity to the disparity in a random dot stereogram. High-resolution maps of the left and right eye receptive fields indicated that both phase and position shifts were common. Single cells usually showed a combination of the two, and the optimum disparity was best correlated with the sum of receptive field phase and position shift.     

Two-dimensional substructure of stereo and motion interactions in macaque visual cortex

The analysis of object motion and stereoscopic depth are important tasks that are begun at early stages of the primate visual system. Using sparse white noise, we mapped the receptive field substructure of motion and disparity interactions in neurons in V1 and MT of alert monkeys. Interactions in both regions revealed subunits similar in structure to V1 simple cells. For both motion and stereo, the scale and shape of the receptive field substructure could be predicted from conventional tuning for bars or dot-field stimuli, indicating that the small-scale interactions were repeated across the receptive fields. We also found neurons in V1 and in MT that were tuned to combinations of spatial and temporal binocular disparities, suggesting a possible neural substrate for the perceptual Pulfrich phenomenon. Our observations constrain computational and developmental models of motion-stereo integration.     

Attention and visual perception

Somewhere between the retina and our conscious visual experience, the majority of the information impinging on the eye is lost. We are typically aware of only either the most salient parts of a visual scene or the parts that we are actively paying attention to. Recent research on visual neurons in monkeys is beginning to show how the brain both selects and discards incoming visual information. For example, what happens to the responses of visual neurons when attention is directed to one element, such as an oriented colored bar, embedded among an array of other oriented bars? Some of this research shows that attention to the oriented bar restricts the receptive field of visual neurons down to this single element. However, other research shows that attention to this single element affects the responses of neurons with receptive fields throughout the visual field. In this review, these two seemingly contradictory results are shown to actually be mutually consistent. A simple computational model is described that explains these results, and also provides a framework for predicting a variety of additional neurophysiological, neuroimaging and behavioral studies of attention.     

End-stopping and the aperture problem: two-dimensional motion signals in macaque V1

Our perception of fine visual detail relies on small receptive fields at early stages of visual processing. However, small receptive fields tend to confound the orientation and velocity of moving edges, leading to ambiguous or inaccurate motion measurements (the aperture problem). Thus, it is often assumed that neurons in primary visual cortex (V1) carry only ambiguous motion information. Here we show that a subpopulation of V1 neurons is capable of signaling motion direction in a manner that is independent of contour orientation. Specifically, end-stopped V1 neurons obtain accurate motion measurements by responding only to the endpoints of long contours, a strategy which renders them largely immune to the aperture problem. Furthermore, the time course of end-stopping is similar to the time course of motion integration by MT neurons. These results suggest that cortical neurons might represent object motion by responding selectively to two-dimensional discontinuities in the visual scene.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist ‘What’ and ‘Where’ pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives ‘where’, for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.     

Colour vision: primary visual cortex shows its influence

New results have revealed that neurons in visual area V1 are influenced by chromatic context, in a way consistent with colour constancy. Other studies have mapped the internal cone-input structure of V1 receptive fields. Put together, these findings suggest important dual roles for V1 in colour perception.     

A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons

We propose a model for the neuronal implementation of selective visual attention based on temporal correlation among groups of neurons. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The spike trains of neurons whose receptive fields donot overlap with the focus of attention are distributed according to homogeneous (time-independent) Poisson process with no correlation between action potentials of different neurons. In contrast, spike trains of neurons with receptive fields within the focus of attention are distributed according to non-homogeneous (time-dependent) Poisson processes. Since the short-term average spike rates of all neurons with receptive fields in the focus of attention covary, correlations between these spike trains are introduced which are detected by inhibitory interneurons in V4. These cells, modeled as modified integrate-and-fire neurons, function as coincidence detectors and suppress the response of V4 cells associated with non-attended visual stimuli. The model reproduces quantitatively experimental data obtained in cortical area V4 of monkey by Moran and Desimone (1985).     

Effect of Contrast on Visual Spatial Summation in Different Cell Categories in Cat Primary Visual Cortex

Multiple cell classes have been found in the primary visual cortex, but the relationship between cell types and spatial summation has seldom been studied. Parvalbumin-expressing inhibitory interneurons can be distinguished from pyramidal neurons based on their briefer action potential durations. In this study, we classified V1 cells into fast-spiking units (FSUs) and regular-spiking units (RSUs) and then examined spatial summation at high and low contrast. Our results revealed that the excitatory classical receptive field and the suppressive non-classical receptive field expanded at low contrast for both FSUs and RSUs, but the expansion was more marked for the RSUs than for the FSUs. For most V1 neurons, surround suppression varied as the contrast changed from high to low. However, FSUs exhibited no significant difference in the strength of suppression between high and low contrast, although the overall suppression decreased significantly at low contrast for the RSUs. Our results suggest that the modulation of spatial summation by stimulus contrast differs across populations of neurons in the cat primary visual cortex.     

Laminar distribution of neurons with different types of receptive fields in the rabbit visual cortex

Investigation of receptive fields of 232 primary visual cortical neurons in rabbits by the use of shaped visual stimuli showed that 21.1% are unselective for stimulus orientation, and 34.1% have simple, 16.4% complex, and 18.5% hypercomplex receptive fields, and 9.9% have other types. Neurons with different types of receptive fields also differed in spontaneous activity, selectivity for rate of stimulus movement, and acuteness of orientational selectivity. Neurons not selective to orientation were found more frequently in layer IV than in other layers, and very rarely in layer VI. Cells with simple receptive fields were numerous in all layers but predominated in layer VI. Neurons with complex receptive fields were rare in layer IV and more numerous in layers V and VI. Neurons with hypercomplex receptive fields were found frequently in layers II + III and IV, rarely in layers V and VI. Spontaneous unit activity in layer II + III was lowest on average, and highest in layer V. Acuteness or orientational selectivity of neurons with simple and complex receptive fields in layers II + III and V significantly exceeded the analogous parameter in layers IV and VI.A. N. Severtsov Institute of Evolutionary Morphology and Ecology of Animals, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 17, No. 1, pp. 19–27, January–February, 1985.     

Border ownership from intracortical interactions in visual area v2

A border between two image regions normally belongs to only one of the regions; determining which one it belongs to is essential for surface perception and figure-ground segmentation. Border ownership is signaled by a class of V2 neurons, even though its value depends on information coming from well outside their classical receptive fields. I use a model of V2 to show that this visual area is able to generate the ownership signal by itself, without requiring any top-down mechanism or external explicit labels for figures, T junctions, or corners. In the model, neurons have spatially local classical receptive fields, are tuned to orientation, and receive information (from V1) about the location and orientation of borders. Border ownership signals that model physiological observations arise through finite range, intraareal interactions. Additional effects from surface features and attention are discussed. The model licenses testable predictions.     

老年猫视皮层细胞对刺激反应的对比敏感度下降伴随皮层内抑制作用减弱(英文)

对人类和动物的心理学研究证实,老年个体的视觉对比敏感度相对青年个体显著下降。为揭示其可能的神经机制,采用在体细胞外单细胞记录技术研究青、老年猫(Felis catus)初级视皮层(primary visual cortex,V1)细胞对不同视觉刺激对比度的调谐反应。结果显示,老年猫V1细胞对视觉刺激反应的平均对比敏感度比青年猫显著下降,这与灵长类报道的研究结果相一致,表明衰老影响视皮层细胞对视觉刺激反应的对比敏感度是灵长类和非灵长类哺乳动物中普遍存在的现象,并可能是介导老年性视觉对比敏感度下降的神经基础。另外,与青年猫相比,老年猫初级视皮层细胞对视觉刺激的反应性显著增强,信噪比下降,感受野显著增大,表明衰老导致的初级视皮层细胞对视觉刺激反应的对比敏感度下降伴随着皮层内抑制性作用减弱。     

Functional characteristics of visual cortical neurons during local photic stimulation of their receptive fields in cats

A group of functional characteristics of 103 neurons in visual cortical area 17 was investigated in acute experiments on curarized, light-adapted cats during a change in various parameters of the local photic stimuli. The average threshold sensitivity of the neuron population was 32 dB (0.052 nit), the sharpness of orientation tuning was 37°, the critical summation time was 57 msec, and the reactivity recovery time 190 msec. Photic sensitivity was lower during light adaptation than during dark adaptation, orientation selectivity of the neurons was increased, temporal summation was lengthened, and the time required by the neuron to recovery from after-inhibition was shortened. Several properties of the cortical neurons depended on the accentricity of their receptive fields: Cells with centrally localized receptive fields on average had lower thresholds and shorter summation time and they recovered their reactivity more quickly; their activity was of a higher frequency and they more often generated short phasic discharges than neurons with receptive fields in the peripheral part of the visual field. The mechanisms responsible for changes in the properties of neurons in the central and peripheral visual channels during dark and light adaptation are discussed. The presence of several inhibitory subsystems in the cortex regulating unit activity in the primary visual projection area is postulated.     

Organization of receptive fields of motion-senstitive neurons in the cat pulvinar

The distribution of 70 visually sensitive neurons in the cat pulvinar sensitive to motion in the receptive fields was studied. The experimental results showed that components with directional characteristics are present in the structure of these fields of both direction-selective and unselective neurons. In the receptive fields of direction-selective neurons the directional elements of the substructure have identical preferred directions, which coincide with the preferred directions of response to stimulus movement over the entire receptive field. The organization of receptive fields of direction-selective neurons of the visual association structure thus does not differ significantly from that of analogous fields of visual projection neurons. Directional elements of the receptive fields of direction-unselective neurons were found to have different preferred directions, thereby providing a basis for organization of the nondirectional response of the neuron to a stimulus moving across the entire receptive field.L. A. Orbeli Institute of Physiology, Academy of Sciences of the Armenian SSR, Erevan. Translated from Neirofiziologiya, Vol. 14, No. 4, pp. 339–346, July–August, 1982.