Neural circuit mechanisms for multisensory associative learning

多感觉联想学习的神经回路机制

基本信息

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
项目类别：
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

项目摘要

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.

项目概要大脑使用感官表征来评估风险并预测奖励，以调整行为。感知是一个多感官过程，为了做出可靠的预测，大脑将其结合起来是有利的。与许多物种一样，有证据表明人类有不止一种感觉方式来代表世界。复杂的学习形式，例如跨模式增强，其中多种刺激的整合来自不同方式的记忆有助于记忆形成和/或改善辨别力，因为研究。主要专注于孤立地研究我们的感官，但关于多感官仍然存在许多问题感觉学习中心的感觉表征规则在不同感觉模式中是否相似？双峰线索的非线性表示背后的电路机制是什么？它们如何影响 mul？要回答这些问题，我们必须能够探测和操纵神经回路多感官整合和学习的领域，这在许多模型生物体中都是具有挑战性的。我们建议利用最近的蘑菇体突触连接图，这是一项经过充分研究的学习果蝇果蝇中心，结合最先进的体内成像和遗传技术实现这一目标的操作技术几乎已经被专门研究过。然而，最近的连接组学数据显示，它收到了大量的信息。我们将确定视觉信息在原则中表示的类型和方式。 MB 的 pal 细胞（目标 1）然后我们将这种特征扩展到复合视觉/嗅觉刺激和表征这些类型信息之间非线性相互作用的电路机制（目标2）。有了这些知识，我们将确定可能引发稳健的多感官学习和使用的刺激参数这些在显微镜下进行学习测定，以探测多感官学习的神经回路（Aim3）。该项目为后续 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。