Neural algorithms underlying diversity in visual feature integration

视觉特征集成多样性背后的神经算法

• 批准号: 10842018
• 负责人: Catherine R von Reyn
• 金额: $ 7.96万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10842018
• 关键词: Algorithms; Behavior; Behavioral; Biological; Biophysics; Computer Models; Data; Development; Drosophila melanogaster; Electrophysiology (science); Genetic; Grant; Invertebrates; Methods; Modeling; Molecular; Motor output; Neurodegenerative Disorders; Neurodevelopmental Disorder; Neurons; Optic Lobe; Output; Pathway interactions; Population; Property; RNA Interference; Sensory; Stimulus; Synapses; System; Technology; Validation; Visual; Visual impairment; behavioral response; blind; cell type; motor behavior; neural; neuroprosthesis; optogenetics; sensory processing disorder; visual information; visual motor

PROJECT SUMMARY We here focus on determining the algorithms that enable highly similar visual information to be transformed into diverse, behaviorally relevant outputs. We also seek to determine the mechanisms that generate these algorithms. Understanding how visual information is transformed into representations relevant for behavior is key for restoring sensorimotor transformations in those who are blind or visually impaired, or suffer from sensory processing disorders. For our visual inputs, we use looming stimuli, the 2-D projections of an object approaching on a direct collision course. Looming stimuli elicit a conserved diversity in behavioral responses across species that are necessary for survival. This diversity is thought to emerge through parallel sensorimotor processing pathways that differentially transform visual features of a looming stimulus into motor outputs. Limited access to both visual feature encoding and visual feature integrating circuit components has however limited the development and biological validation of the algorithms utilized across pathways. We circumvent these limitations by using Drosophila melanogaster that provides the necessary electrophysiological and genetic access to the cell types that participate in these sensorimotor transformations. Our preliminary data suggest looming information is transformed within eight descending sensorimotor pathways (DN) that receive features of looming stimuli from up to six optic lobe columnar projection neuron (OLCPN) cell types. In this interdisciplinary grant, we capitalize on the complementary expertise of Dr. von Reyn (PI), who has pioneered electrophysiological, behavioral, and genetic methods for investigating feature integration within OLCPN and DN, and Dr. Ausborn (co-PI), who has broad expertise in the development of mechanistic biophysical circuit models for the analysis of neural computations within mammalian and invertebrate systems. Here we characterize the extent to which different DN intrinsic properties and circuit mechanisms account for the observed output diversity. In Aim 1 we combine electrophysiology, RNAi silencing, and computational modeling to establish, at a molecular level, intrinsic integration mechanisms for each DN. In Aim 2, we combine electrophysiology, optogenetics, and computational modeling to determine OLCPN synaptic inputs to DN. In Aim 3, through concurrent model and experimental probing, we evaluate the dominant mechanisms that determine looming feature integration algorithms utilized across the DN population. This project will provide a thorough understanding of general principles for transforming sensory information into higher order, behaviorally relevant representations.

项目总结 在这里，我们重点确定能够将高度相似的视觉信息转换为 不同的、行为相关的产出。我们还试图确定产生这些问题的机制 算法。理解视觉信息如何转换为与行为相关的表示是 恢复盲人、视障人士或感觉受损者的感觉运动转换的关键 加工障碍。对于我们的视觉输入，我们使用隐约可见的刺激，即物体接近时的二维投影 在直接相撞的航线上。隐约可见的刺激引发了不同物种之间行为反应的保守多样性 是生存所必需的。这种多样性被认为是通过平行的感觉运动处理而出现的。 将隐约可见的刺激的视觉特征不同地转换为运动输出的通路。有限访问 然而，对于视觉特征编码和视觉特征集成电路组件两者都限制了 开发和生物验证跨路径使用的算法。我们绕过了这些 利用提供必要电生理和遗传学的黑腹果蝇的局限性 获得参与这些感觉运动转换的细胞类型。我们的初步数据显示 隐约可见的信息在8条下行感觉运动路径(DN)内转换，这些下行感觉运动路径接收 来自多达六种视叶柱状投射神经元(OLCPN)细胞类型的隐约可见的刺激。在这一交叉学科中 格兰特，我们利用冯·雷恩博士(PI)的互补专业知识，他开创了 用于研究OLCPN和OCPN内特征整合的电生理、行为和遗传学方法 Dn和Ausborne博士(共同)，他在机械生物物理回路的开发方面拥有广泛的专业知识 哺乳动物和无脊椎动物系统内神经计算的分析模型。在这里我们 描述不同的dN固有属性和电路机制在多大程度上解释了观察到的 输出多样性。在目标1中，我们结合了电生理学、RNAi沉默和计算建模 在分子水平上为每一种糖尿病肾病建立内在整合机制。在目标2中，我们结合了 电生理学、光遗传学和计算模型以确定OLCPN突触传入糖尿病肾病。在AIM 3、通过并发模型和实验探索，我们评估了决定因素的主导机制 跨目录号码人群使用的隐约可见的功能集成算法。这个项目将提供一个彻底的 理解将感官信息转化为更高层次的行为相关信息的一般原则 申述。