Dissemination of tools and methods for modeling state-dependent neural sensory coding

项目摘要 在整个生命过程中，人类和其他动物都会学习自然声学环境中的统计规律。 他们调整自己的听力，以强调声音的特征，这些特征对行为举止很重要 决定。听力正常的人类能够在拥挤的嘈杂场景中感知重要的声音，并 听懂每个人第一次见面时说的话。然而，周围性听力损失的患者或 中央处理障碍在这些具有挑战性的环境中通常会有听力问题，即使声音是 放大的高于感知阈值的。更好地了解健康人和受损者的功能 听觉系统将支持针对这些缺陷的新疗法。 该项目将开发用于研究中枢听觉处理的计算工具。软件库将支持 对大量编码模型进行拟合和评估，以描述 时变的自然听觉刺激和相应的神经反应。许多这样的模式已经成为 它们之间的直接比较相对较少。该项目将使他们的 比较，从而确定对其业绩做出积极贡献的关键特征。这个 系统将采用模块化设计，以便将来自不同型号的有用元素组合成 更具解释力的综合模型。该软件将是开源的，并将支持 来自多种记录方式的数据，包括小规模单一单位电生理和钙 成像数据，以及大规模局域场和脑磁图数据。 除了建立在现有的关于神经编码的假设之外，该系统还将支持机器学习 使用相同的数据集来拟合人工神经网络模型的方法。这些由数据驱动的大型模型 已被证明对广泛的信号处理问题很有价值，但它们的价值及其与现有 神经感觉处理的模型仍有待探索。 感觉处理涉及到大量神经群体的连贯活动。在人群中学习编码 水平，该系统将支持表征多个神经信号的同时活动的模型 识别与声音编码相关的种群活动的潜在子空间。感觉编码也会受到影响 通过行为背景，反映行为需求和更广泛的环境的变化。这个 系统将把行为状态变量合并到模型中，其中编码属性可以通过 行为环境的变化。

Project Summary Throughout life, humans and other animals learn statistical regularities in the natural acoustic environment. They adapt their hearing to emphasize the features of sound that are important for making behavioral decisions. Normal-hearing humans are able to perceive important sounds in crowded noisy scenes and to understand the speech of individuals the first time they meet. However, patients with peripheral hearing loss or central processing disorders often have problems hearing in these challenging settings, even when sound is amplified above perceptual threshold. A better understanding of the function of the healthy and impaired auditory system will support new treatments for these deficits. This project will develop computational tools to study central auditory processing. A software library will support fitting and evaluating a large number of encoding models to describe the functional relationship between a time-varying natural auditory stimulus and the corresponding neural response. Many such models have been proposed, but relatively few direct comparisons have been made between them. This project will enable their comparison, allowing identification of the key features that contribute positively to their performance. The system will have a modular design so that useful elements from different models can be combined into comprehensive models with even greater explanatory power. The software will be open source and will support data from multiple recording modalities, including small-scale single unit electrophysiological and calcium imaging data, as well as large-scale local field and magnetoencephalography data. In addition to building on existing hypotheses about neural coding, the system will support machine learning methods for fitting artificial neural network models using the same datasets. These large, data-driven models have proven valuable for wide ranging signal processing problems, but their value and relation to existing models for neural sensory processing remain to be explored. Sensory processing involves coherent activity of large neural populations. To study coding at the population level, the system will support models that characterize the simultaneous activity of multiple neural signals and identifies latent subspaces of population activity related to sound encoding. Sensory coding is also influenced by behavioral context, reflecting changes in behavioral demands and the more general environment. The system will incorporate behavioral state variables into models, where encoding properties can be modulated by changes in behavioral context.