Neural mechanisms underlying adaptive optimization of visual sensitivity

描述(申请人提供)：感觉生物学的基本原理之一是感觉系统适应环境变化。有人认为，适应应该具有优化对新环境的敏感性的效果。为了使这一前提具体化和精确化，拟议的研究建立在视觉运动知觉的规范理论基础上，该理论认为视觉系统将通过平衡刺激和测量的不确定性来实现最佳适应。这一理论对视觉时空敏感度作为环境统计函数进行了预测：自适应优化应该表现为观察者和潜在的运动敏感神经元时空敏感度的变化。这项拟议的研究将检验这些预测。适应对视觉敏感度的影响将在视觉运动速度的神经元表征的背景下进行检验。使用特定速度的适应将被用作改变环境统计数据的一种手段。这种适应的知觉和神经元后果都将被记录下来，期望敏感度的变化将反映建模的优化过程。这项拟议的研究是心理物理、神经生理学和视觉适应这一主题的理论方法的丰富交织的集合。实验旨在产生前所未有的综合数据，与灵长类视觉系统的时空特性和环境变化的影响有关。这些数据将被用来进一步理解适应的现象学和机制。

DESCRIPTION (provided by applicant): One of the fundamental tenets of sensory biology is that sensory systems adapt to environmental change. It has been argued that adaptation should have the effect of optimizing sensitivity to the new environment. To make this premise concrete and precise, the proposed research builds on a normative theory of visual motion perception, which argues that the visual system will adapt optimally by balancing stimulus and measurement uncertainties. This theory makes predictions about visual spatiotemporal sensitivity as a function of environmental statistics: Adaptive optimization should be manifested as a change in spatiotemporal sensitivity for an observer and for the underlying motion-sensitive neurons. The proposed research will test these predictions. The effects of adaptation on visual sensitivity will be examined in the context of the neuronal representation of speed of visual motion. Adaptation using specific speeds will be used as a means to effect a change in environmental statistics. Both perceptual and neuronal consequences of this adaptation will be recorded, with the expectation that sensitivity changes will reflect the modeled process of optimization. The proposed research constitutes a richly interwoven collection of psychophysical, neurophysiological and theoretical approaches to the topic of visual adaptation. Experiments have been designed to yield an unprecedented body of comprehensive data bearing on the spatiotemporal properties of the primate visual system and the effects of environmental change. These data will be used to further understanding of the phenomenology and mechanism of adaptation.