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Animals detect targets using a variety of visual cues, with the visual salience of these cues determining which environmental features receive priority attention and further processing. Surround modulation plays a crucial role in generating visual saliency, which has been extensively studied in avian tectal neurons. Recent work has reported that the suppression of tectal neurons induced by motion contrasting stimulus is stronger than that by luminance contrasting stimulus. However, the underlying mechanism remains poorly understood. In this study, we built a computational model (called Generalized Linear-Dynamic Modulation) which incorporates independent nonlinear tuning mechanisms for excitatory and inhibitory inputs. This model aims to describe how tectal neurons encode contrasting stimuli. The results showed that: (1) The dynamic nonlinear integration structure substantially improved the accuracy (significant difference (p < 0.001, paired t-test) in the goodness of fit between the two models) of the predicted responses to contrasting stimuli, verifying the nonlinear processing performed by tectal neurons. (2) The modulation difference between luminance and motion contrasting stimuli emerged from the predicted response by the full model but not by that with only excitatory synaptic input (spatial luminance: 89 ± 2.8% (GL_DM) vs. 87 ± 2.1% (GL_DMexc); motion contrasting stimuli: 87 ± 1.7% (GL_DM) vs. 83 ± 2.2% (GL_DMexc)). These results validate the proposed model and further suggest the role of dynamic nonlinear spatial integrations in contextual visual information processing, especially in spatial integration, which is important for object detection performed by birds.

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Stimulus-induced oscillations and synchrony among neuronal populations in visual cortex are well-established phenomena. Their functional role in cognition are, however, not well-understood. Recent studies have suggested that neural synchrony may underlie perceptual grouping as stimulus-frequency relationships and stimulus-dependent lateral connectivity profiles can determine the success or failure of synchronization among neuronal groups encoding different stimulus elements. We suggest that the same mechanism accounts for collinear facilitation and suppression effects where the detectability of a target Gabor stimulus is improved or diminished by the presence of collinear flanking Gabor stimuli. We propose a model of oscillators which represent three neuronal populations in visual cortex with distinct receptive fields reflecting the target and two flankers, respectively, and whose connectivity is determined by the collinearity of the presented Gabor stimuli. Our model simulations confirm that neuronal synchrony can indeed explain known collinear facilitation and suppression effects for attended and unattended stimuli.

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The classical receptive field (CRF) of a spiking visual neuron is defined as the region in the visual field that can generate spikes when stimulated by a visual stimulus. Many visual neurons also have an extra-classical receptive field (ECRF) that surrounds the CRF. The presence of a stimulus in the ECRF does not generate spikes but rather modulates the response to a stimulus in the neuron's CRF. Neurons in the primate Middle Temporal (MT) area, which is a motion specialist region, can have directionally antagonistic or facilitatory surrounds. The surround's effect switches between directionally antagonistic or facilitatory based on the characteristics of the stimulus, with antagonistic effects when there are directional discontinuities but facilitatory effects when there is directional coherence. Here, we present a computational model of neurons in area MT that replicates this observation and uses computational building blocks that correlate with observed cell types in the visual pathways to explain the mechanism of this modulatory effect. The model shows that the categorization of MT neurons based on the effect of their surround depends on the input stimulus rather than being a property of the neurons. Also, in agreement with neurophysiological findings, the ECRFs of the modeled MT neurons alter their center-surround interactions depending on image contrast.

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RESUMEN

Modulation of a neuron's responses by the stimuli presented outside of its classical receptive field is ubiquitous in the visual system. This "surround modulation" mechanism is believed to be critical for efficient processing and leads to many well-known perceptual effects. The details of surround modulation, however, are still not fully understood. One of the open questions is related to the differences in surround modulation mechanisms in different cortical areas, and their interactions. Here we study patterns of surround modulation in primary visual cortex (V1) and middle temporal complex (hMT+) utilizing a well-studied effect in motion perception, where human observers' ability to discriminate the drift direction of a grating improves as its size gets bigger if the grating has a low contrast, and deteriorates if it has a high contrast. We first replicated the findings in the literature with a behavioral experiment using small and large (1.67 and 8.05 degrees of visual angle) drifting gratings with either low (2%) or high (99%) contrast presented at the periphery. Next, using functional MRI, we found that in V1 with increasing size cortical responses increased at both contrast levels. Whereas in hMT+ with increasing size cortical responses remained unchanged or decreased at high contrast, and increased at low contrast, reflecting the perceptual effect. We also show that the divisive normalization model successfully predicts these activity patterns, and establishes a link between the behavioral results and hMT+ â€‹activity. We conclude that surround modulation patterns in V1 and hMT+ â€‹are different, and that the size-contrast interaction in motion perception is likely to originate in hMT+.

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Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells' receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours. Our model simulations and their results show that the established hierarchical contour detection model can adequately fit the characteristics of the biological experiment, quickly and effectively detect the salient contours in complex scenes, and better suppress the unwanted textures.

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Neurons in the primary visual cortex respond to oriented stimuli placed in the center of their receptive field, yet their response is modulated by stimuli outside the receptive field (the surround). Classically, this surround modulation is assumed to be strongest if the orientation of the surround stimulus aligns with the neuron's preferred orientation, irrespective of the actual center stimulus. This neuron-dependent surround modulation has been used to explain a wide range of psychophysical phenomena, such as biased tilt perception and saliency of stimuli with contrasting orientation. However, several neurophysiological studies have shown that for most neurons surround modulation is instead center dependent: it is strongest if the surround orientation aligns with the center stimulus. As the impact of such center-dependent modulation on the population level is unknown, we examine this using computational models. We find that with neuron-dependent modulation the biases in orientation coding, commonly used to explain the tilt illusion, are larger than psychophysically reported, but disappear with center-dependent modulation. Therefore we suggest that a mixture of the two modulation types is necessary to quantitatively explain the psychophysically observed biases. Next, we find that under center-dependent modulation average population responses are more sensitive to orientation differences between stimuli, which in theory could improve saliency detection. However, this effect depends on the specific saliency model. Overall, our results thus show that center-dependent modulation reduces coding bias, while possibly increasing the sensitivity to salient features. NEW & NOTEWORTHY Neural responses in the primary visual cortex are modulated by stimuli surrounding the receptive field. Most earlier studies assume this modulation depends on the neuron's tuning properties, but experiments have shown that instead it depends mostly on the stimulus characteristics. We show that this simple change leads to neural coding that is less biased and under some conditions more sensitive to salient features.

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The neuronal activity evoked by stimuli confined in a receptive field can be modulated by surround stimuli of the extra-classical receptive field (eCRF). The surrounding modulation, hypothesized to be the basis of visual feature integration and figure-ground segregation, has drawn much attention in the field of neuroscience and engineering. However, most studies focused on surround modulation of individual neuronal response. In this study, we analyzed surround modulation of the population response recorded from rat primary visual cortex, and further investigated dynamic functional connectivity modulated by the surrounding stimuli. The functional connectivity was estimated using Granger causality (GC) and then determined by thresholding the p-matrix with different significance α values. Four scalar indexes were calculated to describe the functional connectivity of neuronal population: averaged connection strength (mGC), connection density (D), clustering coefficient (C) and path length (L). The statistical results from 5 rats showed that these network characteristics were dynamically changed during modulation of surrounding stimuli, which suggested that the neuronal population may connect in a dynamic way during modulation of eCRF. We further guessed that the neurons may happened to be organized in a more efficient way underlying surrounding modulation conditions, which helps to process larger images efficiently with the same number of neurons. This study provided new insights for a better understanding of the underlying neural mechanisms responsible for surround modulation.

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