Neural circuit mechanisms for multisensory associative learning

项目摘要 大脑使用感官表征来评估风险并预测奖励，以调整行为。每小时 感知是一个多感官的过程。为了做出可靠的预测，大脑将联合收割机 不止一种感官形态来代表世界。在人类和许多物种中，有证据表明， 复杂的学习形式，如跨模态增强，其中多种刺激的整合 从不同的形式促进记忆的形成和/或提高辨别力。因为研究 主要集中在孤立地研究我们的感官，许多问题仍然与多感官有关。 sory学习。学习中心的感觉表征规则在不同的感觉形态中是否相似？ 什么样的回路机制是双峰线索的非线性表征的基础？这些对穆尔塞有什么影响 感官学习？要回答这些问题，我们必须能够探测和操纵神经回路 在多感官整合和学习的网站，这是具有挑战性的许多模式生物。这里 我们建议利用最近的蘑菇体突触连接图，一个很好的研究学习 果蝇中心，结合最先进的体内成像和遗传学， 操纵技术来实现这一点。蘑菇体几乎只在 嗅觉学习的背景。然而，最近的连接组学数据显示， 视觉输入的一部分。我们将确定什么样的视觉信息以及如何在主视图中表示 MB的pal细胞（Aim 1）。然后，我们将把这种特征扩展到复合视觉/嗅觉刺激， 表征这些类型的信息之间的非线性相互作用的电路机制（Aim 2）。与 这些知识，我们将确定刺激参数可能会引起强大的多感官学习和使用 这些在显微镜下的学习试验中探测多感觉学习的神经回路（Aim 3）。 该项目为后续的TargetedBCP R 01提供了基础，该项目旨在扩展我们的集成 实验和理论方法提取多感官学习的基本原则。

Project Summary The brain uses sensory representations to assess risk and predict reward in order to adjust behavior. Per­ ception is a multisensory process. To make reliable predictions, it is advantageous for the brain to combine more than one sensory modality to represent the world. In humans, as in many species, there is evidence for sophisticated forms of learning, such as crossmodal enhancement, where the integration of multiple stimuli from different modalities facilitates memory formation and/or improves discrimination. Because research has primarily focused on studying our senses in isolation, many questions remain with regards to multisen­ sory learning. Are the rules of sensory representation in learning centers similar across sensory modalities? What circuit mechanisms underlie non­linear representations of bimodal cues? How do these affect mul­ tisensory learning? To answer these questions, we must be able to probe and manipulate neural circuits at the site of multisensory integration and learning, which is challenging in many model organisms. Here we propose to leverage a recent synaptic connectivity map of the mushroom body, a well­studied learning center of the fruit fly Drosophila melanogaster, combined with state of the art in vivo imaging and genetic manipulations techniques to accomplish this. The mushroom body has been almost exclusively studied in the context of olfactory learning. However recent connectomics data has revealed that it receives a large fraction of visual inputs. We will determine what kind and how visual information is represented in the princi­ pal cells of the MB (Aim1). We will then extend this characterization to compound visual/olfactory stimuli and characterize circuit mechanisms for nonlinear interactions between these types of information (Aim2). With this knowledge, we will determine stimulus parameters likely to elicit robust multisensory learning and use these in a learning assay under the microscope to probe neural circuitry for multisensory learning (Aim3). This project provide the foundation for a subsequent TargetedBCP R01 aimed at expanding our integrated experimental and theoretical approaches to extract fundamental principles of multisensory learning.