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Simple Summary: Animals need to identify important visual targets, like food or predators, from a busy background. This ability relies on how their visual system processes and highlights different visual cues. When it comes to birds, the neurons in the optic tectum play a crucial role in this process, particularly through mechanisms known as surround modulation. Our study used a computer computational model to explore how these neurons respond to different types of visual cues like changes in brightness and motion. We discovered that such a process involves complex, nonlinear interactions and inhibitory signals. These findings may help us better understand how birds and other animals detect and focus on important objects from the environment. 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. [ABSTRACT FROM AUTHOR]

Moving target detection in cluttered backgrounds is always considered a challenging problem for artificial visual systems, but it is an innate instinct of many animal species, especially the avian. It has been reported that spatio-temporal information accumulation computation may contribute to the high efficiency and sensitivity of avian tectal neurons in detecting moving targets. However, its functional roles for moving target detection are not clear. Here we established a novel computational model for detecting moving targets. The proposed model mainly consists of three layers: retina layer, superficial layers of optic tectum, and intermediate-deep layers of optic tectum; in the last of which motion information would be enhanced by the accumulation process. The validity and reliability of this model were tested on synthetic videos and natural scenes. Compared to EMD, without the process of information accumulation, this model satisfactorily reproduces the characteristics of tectal response. Furthermore, experimental results showed the proposed model has significant improvements over existing models (EMD, DSTMD, and STMD plus) on STNS and RIST datasets. These findings do not only contribute to the understanding of the complicated processing of visual motion in avians, but also further provide a potential solution for detecting moving targets against cluttered environments. [ABSTRACT FROM AUTHOR]

Simple Summary: Avian can quickly and accurately detect surrounding objects (especially, moving ones). However, it was unknown how different neurons in the optic tectum (OT) in domestic pigeons (Columba livia domestica) processed moving objects compared to static ones. The electrophysiological results showed that the latency of response to moving stimuli was shorter than that to flashed ones, while the firing rates of response to moving stimulus were higher than that to flashed ones. Furthermore, the modeling study demonstrated that the faster and stronger response to a moving stimulus compared to a flashed stimulus may result from the accumulation process across space and time by tectal neurons. This study also sheds new light on the understanding of motion processing by birds. Birds can rapidly and accurately detect moving objects for better survival in complex environments. This visual ability may be attributed to the response properties of neurons in the optic tectum. However, it is unknown how neurons in the optic tectum respond differently to moving objects compared to static ones. To address this question, neuronal activities were recorded from domestic pigeon (Columba livia domestica) optic tectum, responsible for orienting to moving objects, and the responses to moving and flashed stimuli were compared. An encoding model based on the Generalized Linear Model (GLM) framework was established to explain the difference in neuronal responses. The experimental results showed that the first spike latency to moving stimuli was smaller than that to flashed ones and firing rate was higher. The model further implied the faster and stronger response to a moving target result from spatiotemporal integration process, corresponding to the spatially sequential activation of tectal neurons and the accumulation of information in time. This study provides direct electrophysiological evidence about the different tectal neuron responses to moving objects and flashed ones. The findings of this investigation increase our understanding of the motion detection mechanism of tectal neurons. [ABSTRACT FROM AUTHOR]

Simple Summary: Surround modulation is a basic visual attribute of sensory neurons in many species and has been extensively characterized in mammal primary visual cortex, lateral geniculate nucleus, and superior colliculus. Little attention has been paid to birds, which have a highly developed visual system. We undertook a systematic analysis on surround modulation properties of tectal neurons in pigeons (Columba livia). This study complements existing studies on surrounding modulation properties in non-mammalian species and deepens the understanding of mechanisms of figure–background segmentation performed by avians. Surround modulation has been abundantly studied in several mammalian brain areas, including the primary visual cortex, lateral geniculate nucleus, and superior colliculus (SC), but systematic analysis is lacking in the avian optic tectum (OT, homologous to mammal SC). Here, multi-units were recorded from pigeon (Columba livia) OT, and responses to different sizes of moving, flashed squares, and bars were compared. The statistical results showed that most tectal neurons presented suppressed responses to larger stimuli in both moving and flashed paradigms, and suppression induced by flashed squares was comparable with moving ones when the stimuli center crossed the near classical receptive field (CRF) center, which corresponded to the full surrounding condition. Correspondingly, the suppression grew weaker when the stimuli center moved across the CRF border, equivalent to partially surrounding conditions. Similarly, suppression induced by full surrounding flashed squares was more intense than by partially surrounding flashed bars. These results suggest that inhibitions performed on tectal neurons appear to be full surrounding rather than locally lateral. This study enriches the understanding of surround modulation properties of avian tectum neurons and provides possible hypotheses about the arrangement of inhibitions from other nuclei, both of which are important for clarifying the mechanism of target detection against clutter background performed by avians. [ABSTRACT FROM AUTHOR]