Characterizing nonlinear auditory computations

表征非线性听觉计算

• 批准号: 0827695
• 负责人: Kechen Zhang
• 金额: $ 52.5万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0827695&HistoricalAwards=false
• 关键词: Characterizing; nonlinear; auditory; computations

Many neurons in the auditory system respond to sounds nonlinearly; that is, its response to two sounds played simultaneously differs from the sum of its responses to each sound played alone. Nonlinearities are necessary for many computational functions, but unlike nonlinear models that allow closed-form solutions, nonlinear models are often too hard to characterize in practice. To make nonlinear models tractable, this project will combine single-unit recording in awake marmoset monkey with automated online stimulus design by parallel computing. The goal of this stimulus design is not to maximize the firing rate of a neuron, but to extract the most information about the global stimulus-response relationship. Optimal sounds will be designed "on the fly" according to a neuron's response history, with the help of a fast parallel computer whose running time is compatible with the single-unit recording experiment. The proposed research is expected to produce practical and widely applicable methods for characterizing nonlinear sensory neurons. The auditory system is an ideal system for this type of online experiment because sound space is of lower dimensions and allows faster computations. The methods developed here are expected to generalize to nonlinear problems in other sensory modalities.Theory and algorithm development will focus on generating sound stimuli which can either most accurately estimate a given model, or maximally distinguish competing models. Nonlinear models with various degrees of complexity, including neural network models, will be used simultaneously, and contrasted against one another in the automated experiment. The model-based sound design method will be used to characterize complex response properties of neurons in auditory cortex and inferior colliculus of awake marmoset monkey, a vocal primate. This project focuses on the auditory cortex because studies of its pronounced nonlinearities may potentially benefit most from the new method. For comparison the same method will also be applied to the inferior colliculus, the inputs to which are better known, allowing more realistic hierarchical models to be developed. The models obtained from this method should provide a concise summary of the global stimulus-response relationship of a neuron that generalizes across all types of stimuli. Neural network models may also help extract additional information about the connectivity between different neuronal types, thus providing a link between the stimulus-response function and the structure of the underlying neural circuits.

听觉系统中的许多神经元对声音的反应是非线性的；也就是说，它对同时播放的两种声音的反应不同于它对每种声音单独播放的反应的总和。非线性是许多计算函数所必需的，但与允许闭合形式解的非线性模型不同，非线性模型在实践中往往太难描述。为了使非线性模型易于处理，该项目将通过并行计算将清醒猕猴的单单位记录与自动在线刺激设计相结合。这种刺激设计的目标不是最大化神经元的放电率，而是提取关于全局刺激-反应关系的最多信息。在一台运行时间与单单元录音实验兼容的快速并行计算机的帮助下，将根据神经元的反应历史来设计最优的声音。这项拟议的研究有望为描述非线性感觉神经元提供实用和广泛适用的方法。听觉系统是这类在线实验的理想系统，因为声音空间的维度较低，计算速度更快。这些方法有望推广到其他感官模型中的非线性问题。理论和算法的发展将专注于产生声音刺激，这些刺激要么可以最准确地估计给定的模型，要么可以最大限度地区分竞争模型。在自动化实验中，将同时使用包括神经网络模型在内的各种复杂程度的非线性模型，并进行相互对比。基于模型的声音设计方法将用于描述叫声灵长类动物觉醒的绒猴听觉皮质和下丘神经元的复杂反应特性。这个项目的重点是听觉皮质，因为对其明显的非线性的研究可能会从新方法中受益最大。为了进行比较，同样的方法也将应用于下丘，其输入更为人所知，从而可以开发出更现实的分层模型。通过这种方法得到的模型应该提供一个概括了所有类型刺激的神经元的全局刺激-反应关系的简明总结。神经网络模型还可以帮助提取关于不同神经元类型之间的连接的附加信息，从而在刺激反应功能和潜在神经回路的结构之间提供联系。