Neuro-computational modeling of perisaccadicvisual perception and attention

在自然视觉中，人类和非人类灵长类动物每秒都会进行几次眼跳运动，导致视网膜输入发生巨大变化。尽管这些经常是戏剧性的变化，我们的视觉感知仍然非常稳定，我们可以很容易地注意到并指导我们视觉环境中的对象的运动动作。大量的实验数据描述了不同形式的感受野动力学，我们的目标是通过设计和开发神经计算模型来更好地理解这些动力学。我们将研究连接视觉、注意力和眼球运动计划的神经回路，这些神经回路在自然视觉过程中稳定知觉和注意力表征。这个神经模型最初将被用来更好地理解大脑中的过程，但从长远来看，它也将指导我们设计受大脑启发的主动视觉系统。

During natural vision humans and non-human primates make several saccadic eye movements each second that result in large changes in the retinal input. Despite these often dramatic changes, our visual percept remains remarkably stable and we can readily attend to and direct motor actions towards objects in our visual environment. A large set of experimental data describes different forms of receptive field dynamics which we aim to better understand by the design and development of neuro-computational models. We will investigate the neural circuits linking vision, attention and oculomotor planning that stabilize perceptual and attentional representations during natural vision. This neural model will be initially primarily used to better understand the processes in the brain, but in the long run, it will also guide us to design brain-inspired, active vision systems.