Characterizing nonlinear auditory computations

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对单独播放的每种声音的反应之和。非线性对于许多计算函数是必要的，但与允许封闭形式解的非线性模型不同，非线性模型在实践中往往难以表征。为了使非线性模型易于处理，本项目将结合清醒狨猴的单单元记录和并行计算的自动在线刺激设计。这种刺激设计的目标不是最大化神经元的放电速率，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，根据神经元的反应历史，“在飞行中”设计出最佳声音。该研究有望为非线性感觉神经元的表征提供实用且广泛适用的方法。听觉系统是这种在线实验的理想系统，因为声音空间的维度更低，计算速度更快。这里发展的方法有望推广到其他感官模式的非线性问题。理论和算法的发展将集中于产生声音刺激，既可以最准确地估计给定的模型，也可以最大限度地区分竞争模型。不同复杂程度的非线性模型，包括神经网络模型，将同时使用，并在自动化实验中相互对比。基于模型的声音设计方法将被用于描述猕猴听觉皮层和下丘神经元的复杂反应特性。这个项目的重点是听觉皮层，因为研究其明显的非线性可能从新方法中获益最多。为了进行比较，同样的方法也将应用于下丘，下丘的输入更为人所知，从而允许开发更现实的层次模型。从这种方法获得的模型应该提供一个概括所有类型刺激的神经元的整体刺激-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连接的额外信息，从而提供刺激-反应功能与潜在神经回路结构之间的联系。

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.