Interaction of neuronal filters involved in perceptual decisions of grasshoppers

参与蚱蜢感知决策的神经元过滤器的相互作用

• 批准号: 252726299
• 负责人: Professor Dr. Bernhard Ronacher
• 金额: --
• 依托单位: Arbeitsgruppe Neurobiologie des Hörens
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/252726299?language=en
• 关键词: Interaction; neuronal; filters; involved; perceptual

How can a relatively small nervous system process and discern the fast changes which are characteristic for acoustic signals, and how can it attain a high overall reliability in spite of its unreliable components, the neurons? These questions will be investigated using the acoustic communication of grasshoppers as a model system. In these animals, species-specific communication signals (songs) serve to initiate the meeting of sexual partners and reproduction. The decision of whether or not they should respond to a song signal of a potential partner is particularly important for females, due to their high investment into large eggs.Since the auditory pathway of grasshoppers is relatively simply organized it offers the opportunity to understand how information about these highly relevant sensory signals is extracted and represented in a small nervous system. In accord with optimal coding theory we find that responses of different neurons become increasingly decorrelated and sparse early in the auditory pathway of grasshoppers. This resembles the situation at higher stages of sensory systems of vertebrates. The manageable number of auditory neurons allows us to better understand the neuronal operations that guide such transformations in signal representation. A temporally and population-sparse representation of the communication signals probably allows for a more efficient information read-out by downstream neurons and may facilitate behavioral decisions. We began to investigate the decisions that occur on the top of these processing steps by means of a linear-nonlinear (LN)-model approach. Using only two feature detectors, this LN-model yielded a high predictive power, explaining ~ 90% of the variance in behavioral data of the investigated species. In addition, the model explained several so far enigmatic observations of behavioral as well as of neurophysiological experiments. A main part of the proposal will be to search for the neuronal implementations of the specific predictions derived from the LN-model. Furthermore, we will extend this modeling approach to additional stimulus features, and in particular also to other grasshopper species. Closely related species often differ considerably in their song patterns and the corresponding preferences in the signal receivers. These tests aim at revealing the models universality as well as potential constraints. By analyzing the auditory processing in different species we also expect to obtain insights into evolutionary constraints of song evolution and speciation events.

一个相对较小的神经系统如何处理和识别声信号特征的快速变化，以及如何在其不可靠的组件神经元的情况下获得较高的整体可靠性？这些问题将使用蚱蜢的声学通信作为模型系统进行研究。在这些动物中，物种特定的通讯信号（歌曲）用于启动性伴侣的相遇和繁殖。决定是否应对潜在伴侣的歌曲信号对雌性尤其重要，因为它们对大卵的投入很高。由于蚱蜢的听觉通路组织相对简单，因此有机会了解如何在小型神经系统中提取和表示这些高度相关的感觉信号的信息。根据最优编码理论，我们发现在蚱蜢听觉通路的早期，不同神经元的反应变得越来越不相关和稀疏。这类似于脊椎动物感觉系统高级阶段的情况。可管理数量的听觉神经元使我们能够更好地理解指导信号表示的此类转换的神经元操作。通信信号的时间和群体稀疏表示可能允许下游神经元更有效地读出信息，并可能促进行为决策。我们开始通过线性非线性 (LN) 模型方法研究发生在这些处理步骤之上的决策。仅使用两个特征检测器，该 LN 模型就产生了很高的预测能力，解释了所研究物种行为数据中约 90% 的方差。此外，该模型还解释了迄今为止对行为和神经生理学实验的一些神秘观察。该提案的主要部分是寻找从 LN 模型导出的特定预测的神经元实现。此外，我们将把这种建模方法扩展到其他刺激特征，特别是其他蚱蜢物种。密切相关的物种通常在其鸣叫模式和信号接收器的相应偏好方面存在很大差异。这些测试旨在揭示模型的普遍性以及潜在的限制。通过分析不同物种的听觉处理，我们还期望深入了解歌曲进化和物种形成事件的进化限制。