Cocktail Party Problem: Perspective on Neurobiology of Auditory Scene Analysis

描述（由申请人提供）：听觉场景分析的神经生物学的多尺度视角。尽管在过去的几十年里，计算机技术取得了巨大的进步，但仍然有许多任务对孩子来说很容易，但对先进的计算机系统来说却很困难。大多数现有系统面临的一个特殊挑战是处理复杂的声学环境，背景噪声和竞争的谈话者：鸡尾酒会上经常遇到的挑战（Cherry，1953），正式称为听觉场景分析（Bregman，1990）。这一领域的进展具有巨大的影响和长期利益，涵盖医疗、工业、军事和机器人领域;以及改善感官受损和衰老大脑的通信辅助设备（助听器、耳蜗植入物、基于语音的人机界面）。尽管它的重要性，工程和感知科学，听觉场景分析的神经基础的研究仍然处于起步阶段。这一领域尤其受到缺乏整合理论的挑战，这些理论将我们对场景分析的感知基础的知识与听觉通路沿着各个阶段的神经机制结合起来。由于问题的性质，神经回路在发挥作用是复杂的和多尺度的设计。提出的研究的目标是提供一个系统的观点，建模场景分析，集成机制在单个神经元水平，人口水平和跨区域的相互作用。所提出的理论的智力价值在于阐明了具体的机制和计算规则，促进了其在工程系统中的集成，并能够产生新的可测试的预测。该提案调查了一个关键假设，即对复杂声音特征的关注会实例化与该特征相关的所有元素，从而将它们结合在一起作为一个感知“对象”或流。这个“绑定假说”需要三个尺度的分析：一个微观层面的映射复杂的声音到一个多维的皮质功能表示;一个中观层次的连贯性分析相关的活动在人群中的皮质神经元;和宏观层面的反馈过程的注意力和期望，介导听觉对象的形成。我们将制定这一假设在一个多尺度的计算框架，提供了一个统一的理论听觉场景分析的神经基础。该项目的三个核心研究目标采用计算和生理方法探索该模型的各个方面：多尺度相干模型：主要目标是制定的“绑定假说”作为一个统一的生物学合理的理论听觉流，整合多尺度的感觉与认知皮层机制。这项计算工作将结合目标II和III的实验结果，生成可测试的预测，并提供有效的算法实现，以解决生物医学应用中的“鸡尾酒会问题”;目标II。多尺度相干理论的生理学研究：我们的目标是使用动物模型来记录初级听觉和前额叶皮层中的单个单元（微观水平，中观水平）和跨区域（宏观水平）生理活动，同时呈现足够复杂的声学环境，以便测试和改进计算模型;目标III。在人类的生理和感知测试中完善相干理论：目标是使用脑磁图（MEG）和心理声学实验直接测试人类受试者模型的预测。我们将特别关注在正常和老化的大脑中，皮质机制在场景分析中的作用。拟议的研究利用了一个整合神经生物学和工程学的跨学科团队的专业知识。它的独特之处在于，它是第一次努力假设的场景分析问题的连贯性的作用，并调查“绑定假说”整合皮层和注意力机制的听觉流实验。此外，通过直接在人类受试者身上测试该理论，并比较正常和衰老的大脑（已知在鸡尾酒会环境中面临感知困难），我们希望更好地了解场景分析在正常和故障状态下的神经基础，从而增强模型的转化潜力。这一努力的更广泛的影响是提供多功能和易于处理的听觉流分离模型，大大促进了工程系统中的这种能力的集成。

DESCRIPTION (provided by applicant): A Multi-scale Perspective on the Neurobiology of Auditory Scene Analysis A. Aims and Significance Despite the enormous advances in computing technology over the last decades, there are stills many tasks that are easy for a child, yet difficult for advanced computer systems. A particular challenge to most existing systems is dealing with complex acoustic environments, background noises and competing talkers: A challenge often experienced in cocktail parties (Cherry, 1953) and formally referred to as auditory scene analysis (Bregman, 1990). Progress in this field has tremendous implications and long- term benefits covering the medical, industrial, military and robotics domains; as well as improving communication aids (hearing aids, cochlear implants, speech-based human-computer interfaces) for the sensory-impaired and aging brains. Despite its importance for both engineering and perceptual sciences, the study of the neural underpinnings of auditory scene analysis remains in its infancy. This field is particularly challenged by the lack of integrative theories which incorporate our knowledge of the perceptual bases of scene analysis with the neural mechanisms along various stages of the auditory pathway. Because of the nature of the problem, the neural circuitry at play is intricate and multi-scale by design. The objective of the proposed research is to provide a systems view to modeling scene analysis which integrates mechanisms at the single neuron level, population level and across area interactions. The intellectual merit of the proposed theory is to elucidate the specific mechanisms and computational rules at play; facilitate its integration in engineering systems and enable generating novel testable predictions. The proposal investigates the key hypothesis that attention to a feature of a complex sound instantiates all elements that are coherent with this feature, thus binding them together as one perceptual "object" or stream. This "binding hypothesis" requires three scales of analyses: a micro-level mapping of complex sounds into a multidimensional cortical feature representation; a meso-level coherence analysis correlating activity in populations of cortical neurons; and macro-level feedback processes of attention and expectations that mediate auditory object formation. We shall formulate this hypothesis within a multi-scale computational framework that provides a unified theory for the neural underpinnings of auditory scene analysis. The three core research aims of this project explore all facets of this model employing computational and physiological approaches: Aim I. A multi-scale coherence model: The main goal is to formulate the "binding hypothesis" as a unified biologically plausible theory of auditory streaming, integrating multi-scale sensory with cognitive cortical mechanisms. This computational effort will incorporate findings from experiments in Aims II and III, generate testable predictions, as well as provide effective algorithmic implementations to tackle the "cocktail party problem" in biomedical applications; Aim II. Physiological investigations of the multi-scale coherence theory: Our aim is to use an animal model to record single-unit (micro-level, meso-level) and across area (macro-level) physiological activity in both primary auditory and prefrontal cortex, while presenting sufficiently complex acoustic environments so as to test and refine the computational model; Aim III. Refinement of the coherence theory with physiological and perceptual testing in humans: The objective is to directly test predictions from the model in human subjects, using magnetoencephalography (MEG) and psychoacoustic experiments. We shall particularly focus on the role of cortical mechanisms in scene analysis in normal and aging brains. The proposed research draws upon the expertise of a cross-disciplinary team integrating neurobiology and engineering. It is unique in that it is the first effort to postulate a role for coherence in the scene analysis problem, and to investigate the "binding hypothesis" integrating cortical and attention mechanisms in auditory streaming experiments. In addition, by testing the theory directly on human subjects and comparing normal and aging brains (known to face perceptual difficulties in cocktail party settings), we hope to better understand the neural underpinnings of scene analysis under their normal and malfunctioning states, hence enhancing the translational potential of the model. The broader impact of this effort is to provide versatile and tractable models of auditory stream segregation, significantly facilitating the integration of such capabilities in engineering systems.