Visual Adaptation and Neuronal Selectivity

视觉适应和神经元选择性

• 批准号: 8158147
• 负责人: David A Leopold
• 金额: $ 44.32万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8158147
• 关键词: Neurons; visual adaptation

It is now possible to design computers that compute millions of calculations per second, perform useful tasks, and recognize objects. However, despite these advances, computers fall short of emulating brain function in the domain of flexibility. One of the remarkable aspects of the brain is that it interprets stimuli and organizes its actions in a highly situation-dependent and flexible manner. With regard to visual stimuli, the brain is able to learn the structure and significance of a large number of stimulus categories. For some categories, such as faces, its performance is utterly remarkable: we readily can discriminate between and recognize thousands of different individuals based on very subtle differences in the face components and their geometrical configuration. This is all the more impressive given that each time we see a given face, its image on our retina is different from the last time. Two different individuals seen from the same distance and same lighting conditions may cast very similar retinal images, at least at a coarse level, than the same individual seen twice under different conditions. Nonetheless, we are able to fluidly and effortlessly recognize people, objects, landmarks, and scenes based on a single glance. How does this ability come about? One answer to this question relates to the manner in which complex visual stimuli are encoded in the brain. This topic has been a central focus of our research. In the past year, we have published papers related to the neural representation of stimuli in object-encoding regions V4 and TE of the visual cortex. We have previously shown that individual faces are systematically encoded based on their distinctiveness relative to an average face, so called norm-based encoding. In other words, the brain encodes to a given face according to how it differs in its structure from a mean, prototypical face. We first provided evidence for this means of encoding by conducting human behavioral experiments involving visual adaptation. In those experiments the presentation of one face for a few seconds altered the way a subsequently presented face was perceived. The misperceptions closely matched the expectations of norm-based encoding. This was strengthened by more recent neurophysiological recordings in nonhuman primates showing the neurons in the inferotemporal cortex adjust their firing rate based on the relative difference of a face from the average of many faces. Both lines of research point to the conclusion that the brain encodes face identity systematically, and relative to a prototypical average. A second important feature of face perception is the ability to learn and remember new faces, a process that undoubtedly involves changes in the brain. Unlike some skills (e.g. language acquisition), our capacity to learn new faces remains strong into adulthood. This implies that the neural machinery underlying our recognition remains, in a sense, plastic. How does experience modify neural responses? During the past year we have begun to approach this problem by monitoring the tuning functions of individual neurons over periods of days and weeks. While recording from single cells is a routine process, monitoring them for extended periods of time poses enormous challenges. We have overcome these challenges by developing, with the help of an outside collaborator, a novel inertialess microelectrode bundle array, which maintains close proximity to individual neurons by moving with the small movements of the brain. The advantage of this approach is that in recording the responses of isolated neurons over a period of many days the effects of visual learning on neural selectivity can be assessed. In the laboratory, nonhuman primates are presently being trained to learn new categories of stimuli, including novel human and simian faces, as neural responses are monitored. The results from this study will shed light on how we are able to learn stimuli because of changes in the selectivity of visual neurons.

现在可以设计计算每秒计算数百万计算的计算机，执行有用的任务并识别对象。 但是，尽管有这些进展，但计算机仍未模仿灵活性领域的大脑功能。 大脑的非凡方面之一是它解释刺激并以高度依赖和灵活的方式组织其行为。 关于视觉刺激，大脑能够学习大量刺激类别的结构和意义。 对于某些类别（例如面孔），它的性能是完全出色的：我们可以根据面部成分非常细微的差异及其几何配置来区分和识别数千个不同的个体。 鉴于每次我们看到给定的面孔时，它在视网膜上的形象与上一次不同，这更加令人印象深刻。 从相同的距离和相同的照明条件下看到的两个不同的个体可能比在不同条件下看到两次相同的个体可能呈现出非常相似的视网膜图像。 尽管如此，我们能够基于单一的目光来毫不费力，毫不费力地认识人，物体，地标和场景。 这种能力是如何产生的？ 这个问题的一个答案涉及复杂的视觉刺激在大脑中编码的方式。 这个主题一直是我们研究的重点。 在过去的一年中，我们发表了与对象编码区域V4和视觉皮层TE中刺激的神经表示有关的论文。 我们以前已经表明，单个面孔是根据平均面部的独特性系统编码的，所谓的基于规范的编码。换句话说，大脑根据其结构与均值，典型的面部的差异如何编码为给定的面部。 我们首先通过进行涉及视觉适应的人类行为实验提供了这种编码的证据。 在那些实验中，一张面孔在几秒钟内的表现改变了随后呈现的面孔的方式。 误解与基于规范的编码的期望非常匹配。 非人类灵长类动物中的神经生理记录得到了加强，该记录显示了基于脸部的相对差异与许多面部的平均水平的相对差异。 两条研究线都表明，大脑系统地编码面部身份，并且相对于典型平均水平。 面部感知的第二个重要特征是学习和记住新面孔的能力，这一过程无疑涉及大脑的变化。 与某些技能（例如语言获取）不同，我们学习新面孔的能力仍然很强。 这意味着从某种意义上说，我们的认可的神经机制仍然存在。 经验如何修改神经反应？在过去的一年中，我们开始通过监测几天和几周的单个神经元的调整功能来解决此问题。 虽然从单细胞记录是一个例行过程，但监视它们长时间会带来巨大的挑战。 我们通过在外部合作者的帮助下开发了一种新颖的惯性微电极束阵列来克服这些挑战，该捆绑阵列通过与大脑的小动作一起移动，从而保持了与个别神经元的紧密距离。这种方法的优点是，可以评估视觉学习对神经选择性的影响在记录孤立神经元的响应时。 在实验室中，由于对神经反应的监测，非人类灵长类动物目前正在接受培训，以学习新的刺激类别，包括新颖的人和猿猴面孔。 这项研究的结果将阐明我们如何学习刺激，因为视觉神经元的选择性变化。