CRCNS: Optimality principles of auditory representations

描述(由申请人提供)：我们的项目将集中在听觉系统中的感觉表征，有两个总体目标。首先，我们将采用一种实验策略，呼吁对听觉系统进行最佳探测。我们将通过改进最近由同一团队的合作研究使之可行的优化设计方法，来确定不同空间位置的复杂声音刺激如何在清醒的猕猴听觉通路的多个阶段表现出来。这种方法在神经生理记录期间在线工作，通过产生声音刺激来最大化获得关于分层神经网络模型的信息。从在线实验获得的模型提供了对三维空间中丰富声音刺激的复杂听觉反应的准确描述，同时我们也获得了一个合理的网络解释，解释了在听觉通路中较低水平的多个声音定位线索组合如何在下丘和听觉皮质产生复杂的反应特性。其次，我们将从神经种群针对现实世界的情况进行优化的假设开始，我们将确定在实验中测量的神经元反应特性的丰富多样性和异质性是否可以用它们实际上形成针对自然声音优化的有效种群的假设来解释。预期的结果是对神经元群体的复杂性进行原则性的计算解释，以进行合理的定位，包括与各种功能细胞类型相关的所有多样性和变异性。 智力优势：了解神经活动和外界刺激之间的关系是系统神经科学的基本目标之一。我们的合作研究可能有助于解决听神经科学中一个长期存在的问题，即听觉皮质和下丘的神经元反应如何代表整个360�方位和海拔范围内的空间。我们的方法是非常普遍的，也应该很容易地适用于其他感官形式。特别是，优化设计方法可以尽可能快地获得刺激反应场景的全局图景，这种快速功能对于处理清醒和行为正常的动物，甚至是人类特别有价值。我们的结果可以扩展到许多学科，无论何时需要有效地探测一个参数化的输入-输出系统，或者优化一组传感器。例如，这里开发的想法和方法可以用于指导实际的神经形态工程设计或优化人工传感器的种群，这可能会有许多实际应用。我们的结果还将提供坚实的概念基础和一套建模工具，以确定听觉系统在皮质和皮质下的异常或疾病状态，以便处理复杂的声音信号。这一进展可能导致各种神经疾病的可行治疗方法的开发，并可能通过在声音定位线索的帮助下更好地解析复杂的听觉场景来促进神经假体的发展。 更广泛的影响：这个项目在几个层面上对教学做出了贡献。它将为两名研究生提供研究培训，这些研究的结果预计将构成他们的博士论文的大部分。此外，一名博士后研究员将接受计算神经科学合作研究方面的研究培训。将努力让学生和研究人员在广泛的人群中参与，并将广泛宣传这些职位，以确保合格的任职人数不足的候选人意识到机会。这项研究还将纳入我们的教育努力，将结果纳入约翰·霍普金斯大学为研究生和本科生开发的课程。培训、教育和推广部分将直接影响大量学生和大学以外的其他群体，使他们接触到神经生理学和计算神经科学方面的公开问题和跨学科研究方法。私人投资和共同投资致力于在研究和教育方面提高妇女和代表性不足的少数群体的地位。此外，拟议的工作将通过开创基于闭环系统自动刺激设计的新记录技术来加强我们进一步研究的基础设施。这项研究将在许多场所传播，包括国家和国际会议以及同行评议的期刊。如果适用，我们的结果也将在流行的、非技术性的文献中传播。与这项工作有关的所有软件将在因特网上免费提供，为该项目收集的数据的适当子集将在CRCNS资助的数据共享设施上提供。

DESCRIPTION (provided by applicant): Our project will be focused on sensory representations in the auditory system with two overall objectives. First, we will adopt an experimental strategy that calls for optimal probing of the auditory system. We will determine how complex sound stimuli at different spatial locations are represented at multiple stages of the auditory pathway in awake marmoset monkeys by improving an optimal design approach recently made feasible by the collaborative research by the same team. This method works online, during neurophysiological recording, by generating sound stimuli that maximize the information gained about a hierarchical neural network model. A model attained from the online experiment provides an accurate description of complex auditory responses to rich sound stimuli in three dimensional space, and at the same time we also obtain a plausible network explanation of how complex response properties in inferior colliculus and auditory cortex might arise from combining multiple sound localization cues at lower levels in the auditory pathway. Second, we will start with the hypothesis that neural populations are optimized for real world situations, and we will determine whether the rich variety and heterogeneity of neuronal response properties measured in experiments can be explained by the hypothesis that they actually form an efficient population optimized for natural sounds. The expected outcome is a principled computational explanation for the complexity of neuronal populations for sound localization, including all the diversity and variability associated with various functional cell types. Intellectual merit: Understanding the relationship between neural activity and the stimuli from the external world is one of the basic goals in systems neuroscience. Our collaborative research may help resolve a longstanding problem in auditory neuroscience concerning how neuronal responses in auditory cortex and inferior colliculus represent space over the full 360� range of azimuths and elevations. Our approach is very general and should apply readily to other sensory modalities as well. In particular, the optimal design method can obtain a global picture of the stimulus-response landscape as fast as possible, and this speedy feature is especially valuable for working with awake and behaving animals, and even humans. Our results may extend to many disciplines whenever one needs to efficiently probe a parameterized input-output system, or to optimize a population of sensors. For example, the ideas and the methods developed here could be used to guide practical neuromorphic engineering designs or to optimize populations of artificial sensors, which may find many practical applications. Our results will also provide a solid conceptual basis and a set of modeling tools to qualify abnormal or diseased states of the auditory system at both the cortical and subcortical levels for processing of complex sound signals. This progress could lead to the development of viable therapies for various neurological disorders, and it could improve the development of neural prostheses by better parsing complex auditory scenes with the help of sound localization cues. Broader impact: This project contributes to teaching at several levels. It will provide research training to two graduate students and the results of these studies are expected to constitute the bulk of their Ph.D. theses. In addition, a postdoctoral fellow will receive research training in collaborative research in computational neuroscience. Efforts will be made to include participation from students and researchers over a wide demographic, and the positions will be broadly advertised to ensure that qualified underrepresented candidates are aware of the opportunities. This research will also be integrated into our educational efforts by incorporating results into courses developed for both graduate and undergraduate students at Johns Hopkins University. The training, educational and outreach components will directly affect a large number of students and other groups outside of the university by exposing them to open problems and interdisciplinary research methods in neurophysiology and computational neuroscience. The PI and co-PIs are committed to advancement of women and underrepresented minorities in research and education. Furthermore, the proposed work will strengthen our infrastructure for further studies by pioneering new recording techniques that are based on closed-loop automated stimulus design. The research will be disseminated in many venues, including national and international meetings and peer reviewed journals. When applicable, our results will be disseminated in popular, non-technical literature as well. All software associated with this work will be made freely available on the internet, and appropriate subsets of the data collected for this project will be made available on the CRCNS-funded data sharing facility.