Populationskodierung natürlicher Stimuli

感觉神经科学的基本目标之一是准确地了解有关外部世界的信息如何在大脑活动中表现出来。破解这种“神经密码”对于理解大脑如何做出决定和决定行为，以及开发临床策略以取代因创伤或疾病而失去的大脑功能至关重要。尽管在过去的几十年里，我们对感觉功能的理解取得了巨大的进步，但自然环境中典型的复杂刺激的神经表征仍然是一个谜。我们理解的这一差距可以归因于感觉系统的两个复杂特性，这两个特性在自然环境中很重要，但在涉及记录单个神经元对简单刺激的反应的典型实验室实验中却没有得到解决：（1）感觉神经元的反应特性不是静态的，而是不断地适应于匹配刺激的当前统计特性，以及（2）复杂刺激的表示不限于单个神经元的响应，而是分布在一个高度相互关联的群体的响应中。我计划与我的研究小组一起研究哺乳动物听觉系统中的这些复杂特性，特别是听觉中脑的下丘（IC）。我们将同时记录在复杂的自然刺激的演示过程中的小群体的神经元的反应，我们将使用各种系统识别，信号处理和信息理论技术来表征这些神经元的响应特性。我们还将使用这些实验结果来开发一个模型，该模型将适应和人口相互联系的影响，在预测自然刺激的听觉反应。这项研究的结果将提供有关复杂声音处理的基本信息，并将为阐明真正的神经代码迈出重要一步。

One of the fundamental goals of sensory neuroscience is to understand exactly how information about the outside world is represented in brain activity. Cracking this ‘neural code’ is essential for understanding how the brain makes decisions and determines behavior, as well as for the development of clinical strategies for replacing brain function lost to trauma or disease. Despite tremendous advances in our understanding of sensory function over the past several decades, the neural representation of complex stimuli typical of the natural environment remains a mystery. This gap in our understanding can be attributed to two complex properties of sensory systems that are important in the natural environment, but are not addressed in typical laboratory experiments involving recordings of the responses of a single neuron to simple stimuli: (1) the response properties of sensory neurons are not static, but are constantly adapted to match the current statistical properties of the stimulus and (2) the representation of complex stimuli is not confined to the response of a single neuron, but is distributed across the responses of a highly interconnected population. With my research group, I plan to study these complex properties in the mammalian auditory system, specifically in the inferior colliculus (IC) of the auditory midbrain. We will record the responses of small populations of neurons simultaneously during the presentation of complex natural stimuli and we will characterize the response properties of these neurons using a variety of system identification, signal processing, and information theoretic techniques. We will also use these experimental results to develop a model that incorporates the effects of adaptation and population interconnectivity in predicting auditory responses to natural stimuli. The results of this investigation will provide fundamental information about the processing of complex sounds and will provide an important step toward elucidating the true neural code.