Neural circuit mechanisms for multisensory associative learning

多感觉联想学习的神经回路机制

• 批准号: 10524400
• 负责人: Roudabeh Behnia
• 金额: $ 72.91万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10524400
• 关键词: Adaptive Behaviors; Affect; Animal Model; Animals; Anus; Architecture; Behavior; Biological Assay; Brain; Brain region; Cells; Complex; Cues; Data; Data Set; Decision Making; Discrimination; Drosophila genus; Drosophila melanogaster; Environment; Exhibits; Feedback; Foundations; Human; Imaging Techniques; Investigation; Knowledge; Lead; Learning; Lobe; Logic; Maps; Memory; Microscope; Modality; Modeling; Mushroom Bodies; Neurons; Neurosciences; Olfactory Learning; Olfactory Pathways; Outcome; Pathway interactions; Population; Process; Research; Rewards; Risk; Sensory; Signal Transduction; Site; Stimulus; Stream; Structure; Synapses; Techniques; Theoretical model; Time; Visual; Work; base; classical conditioning; conditioning; connectome; connectome data; experience; experimental study; genetic manipulation; improved; in vivo imaging; interdisciplinary approach; multimodality; multisensory; neural circuit; neuromechanism; olfactory stimulus; predictive modeling; presynaptic; recruit; relating to nervous system; response; sensory system; stem; tool; visual information; visual stimulus

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.

项目摘要 大脑使用感觉表征来评估风险和预测回报，以调整行为。按： 突触是一个多感官过程。为了做出可靠的预测，大脑结合起来是有利的 不止一种感官形态来代表世界。在人类身上，就像在许多物种中一样，有证据表明 复杂的学习形式，如跨模式增强，其中多个刺激的整合 来自不同形态的信息有助于记忆的形成和/或提高辨别能力。因为研究 主要集中在孤立地研究我们的感官，但关于多感官的许多问题仍然存在 学有所成。不同感觉通道的学习中心的感觉表征规则是否相似？ 双峰提示的非线性表示背后的电路机制是什么？这些因素如何影响多个 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现场，这在许多模式生物中是具有挑战性的。这里 我们建议利用最近的蘑菇体突触连接图，这是一种研究得很好的学习方法 果蝇黑腹果蝇中心，结合最先进的活体成像和遗传学 操纵技术来实现这一点。对蘑菇体的研究几乎只在 嗅觉学习的背景。然而，最近的连接学数据显示，它收到了大量的 视觉输入的一小部分。我们将确定视觉信息在原理中表现的类型和方式 MB的PAL细胞(Aim1)。然后我们将把这一特征扩展到复合视觉/嗅觉刺激和 描述这些类型信息之间的非线性相互作用的电路机制(AIM2)。使用 这些知识，我们将确定刺激参数，可能引发强大的多感官学习和使用 这些是在显微镜下的学习测试，以探索多感官学习的神经回路(Aim3)。 该项目为后续的TargetedBCP R01奠定了基础，旨在扩展我们的集成 提取多感官学习基本原理的实验和理论方法。