Mapping of spatiotemporal code features to neural and perceptual spaces

项目摘要 感觉神经科学的两个最基本的问题是：1)刺激信息是如何 以不同信息处理水平的神经元群体的活动为代表？和2)什么 这项活动的特点是在神经处理的下一个层次上阅读以指导行为？第一个问题 一直是不同感官刺激的大量研究的主题。要回答第二个问题， 人们需要在神经元活动和行为之间建立因果联系。在许多系统中感官 信息由神经元活动的复杂时空模式来表示。新颖的录制和 刺激技术很快将允许对成百上千个个体进行精确的时间控制 然而，神经元，寻找神经不同时空特征相关性的概念性方法 代码仍然落后。 为了开发一种新的方法，我们选择哺乳动物的嗅觉作为模型系统，因为气味刺激唤起 肾小球活动的复杂模式，其空间和时间尺度与现有成像完全兼容 和模式刺激技术。此外，单元格在下一个处理级别中的可访问性，即 二尖瓣/簇状细胞从嗅小球获得输入，并将信号传输到高级大脑区域，允许 对具有已知行为相关性的神经活动的不同特征进行编码的系统研究。 我们提出了一种新的方法，将时空编码特征映射到神经和感知空间。首先，我们 用人工和完全参数化光发生模式刺激取代感觉驱动的神经激活。 通过改变这种刺激的参数并记录刺激的行为结果，我们 将建立一个详细的、经过经验验证的神经功能不同特征相关性的数学模型 活动。然后，我们将测试该模型对自然气味的刺激，并探索这些特征是如何处理的 并由下一级处理进行编码。 该项目的成功执行将产生第一个(据我们所知)因果关系验证的模型 分布式神经代码的行为相关性。它将照亮嗅觉中长期存在的问题 加工，从行为关联性的角度探讨嗅觉编码。建议数 该方法可以进一步应用于不同神经系统的多神经元记录和刺激 技巧。

Project Summary Two of the most fundamental questions of sensory neuroscience are: 1) how is stimulus information represented by the activity of populations of neurons at different levels of information processing? and 2) what features of this activity are read at the next levels of neural processing to guide behavior? The first question has been the subject of a large body of work across different sensory stimuli. To answer the second question, one needs to establish a causal link between neuronal activity and behavior. In many systems sensory information is represented by complex spatiotemporal patterns of neuronal activity. Novel recording and stimulating technology will soon allow the precise temporal control of hundreds and thousands of individual neurons, however, conceptual approaches of finding relevance of different spatiotemporal features of neural code still lag behind. To develop a new approach we chose the mammalian olfaction as a model system, because odor stimuli evoke complex patterns of glomerular activity with spatial and temporal scales fully compatible with existing imaging and pattern stimulation technologies. In addition, the accessibility of the cells in the next processing level, the mitral/tufted cells which get input from olfactory glomeruli and transmit the signal to higher brain areas, allows a systematic study of encoding different features of neural activity with known behavioral relevance. We propose a novel approach to map spatiotemporal code features to neural and perceptual spaces. First, we substitute sensory-driven neural activation by artificial and fully parametrized optogenetic pattern stimulation. By varying the parameters of such stimulation and recording the behavioral outcomes of the stimulation, we will build a detailed empirically-validated mathematical model of the relevance of different features of neural activity. Then we will test this model for natural odor stimuli, and explore how these features are processed and encoded by the next level of processing. Successful execution of the project will produce the first (to our knowledge) causally validated model for behavioral relevance of a distributed neural code. It will shine light on long standing questions in olfactory processing, approaching the olfactory code from the perspective of its behavioral relevance. The proposed approach can be further applied to different neural systems using multi-neuronal recording and stimulation techniques.