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: 10474664
• 负责人: Catherine R von Reyn
• 金额: $ 1.55万
• 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/10474664
• 关键词: Afferent Neurons; Algorithms; Anatomy; Animals; Behavior; Behavioral; Behavioral Genetics; Biological; Biophysical Process; Biophysics; Cells; Code; Comb animal structure; Computer Models; Data; Development; Drosophila melanogaster; Electrophysiology (science); Environment; Genetic; Grant; Individual; Invertebrates; Ion Channel; Knowledge; Link; Location; Maps; Methods; Modeling; Molecular; Motor; Motor output; Neurodegenerative Disorders; Neurodevelopmental Disorder; Neurons; Neurosciences; Optic Lobe; Outcome; Output; Pathway interactions; Pattern; Play; Population; Process; Property; RNA Interference; Research; Resolution; Role; Sensory; Shapes; Stimulus; Synapses; System; Technology; Testing; Validation; Visual; Visual impairment; behavioral response; biophysical model; blind; cell type; combinatorial; insight; knock-down; motor behavior; neural circuit; neuroprosthesis; optogenetics; presynaptic; programs; relating to nervous system; response; sensory processing disorder; visual information; visual motor; visual stimulus

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.

项目摘要 在这里，我们专注于确定算法，使高度相似的视觉信息转化为 多样的、行为相关的输出。我们还试图确定产生这些的机制， 算法理解视觉信息如何被转换成与行为相关的表示， 恢复盲人或视力受损者或患有感觉运动障碍者的感觉运动转换的关键 加工障碍对于我们的视觉输入，我们使用若隐若现的刺激，一个物体接近的二维投影 在直接碰撞的航线上隐现刺激引发了跨物种行为反应的保守多样性 这是生存所必需的。这种多样性被认为是通过平行的感觉运动处理而出现的 这些通路将隐约可见的刺激的视觉特征差异地转换成运动输出。机会有限 然而，对于视觉特征编码和视觉特征集成电路组件， 开发和生物学验证跨途径使用的算法。我们绕过这些 限制通过使用果蝇，提供必要的电生理和遗传 参与这些感觉运动转换的细胞类型。我们的初步数据显示 隐现的信息在八条下行感觉运动通路（DN）中转化， 来自多达六种视叶柱状投射神经元（OLCPN）细胞类型的隐现刺激。在这个跨学科的 授予，我们利用冯雷恩博士（PI）的互补专业知识，他开创了 研究OLCPN内特征整合的电生理、行为和遗传方法， DN和Ausborn博士（合作PI），他在机械生物物理电路的开发方面具有广泛的专业知识 哺乳动物和无脊椎动物系统中神经计算的分析模型。这里我们 表征不同的DN固有特性和电路机制在多大程度上解释了所观察到的 输出分集在目标1中，我们将联合收割机电生理学、RNAi沉默和计算建模结合起来， 在分子水平上建立每个DN的内在整合机制。在目标2中，我们将联合收割机 电生理学、光遗传学和计算建模来确定对DN的OLCPN突触输入。在Aim中 3、通过并发模型和实验探索，我们评估了决定 在DN人群中使用的隐现特征集成算法。该项目将提供一个全面的 理解将感官信息转化为更高层次的、与行为相关的信息的一般原则 表示。