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How do auditory cortical neurons represent ethologically relevant natural stimuli? Characterizing stimulus feature selectivity and invariance

听觉皮层神经元如何代表行为学相关的自然刺激？

基本信息

批准号：
BB/N008731/1
负责人：
Andriy Kozlov
金额：
$ 43.08万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FN008731%2F1
关键词：
How do auditory cortical neurons

项目摘要

How sensory systems represent natural signals is a long-standing classical problem in neuroscience. Using stimuli that are relevant to the animal produced the clearest descriptions of how sensory neurons work in "specialized animals", such as bats, the electric fish, and the barn owl. In each of these examples, the stimuli were both natural and simple, which was key to understanding their representations. Most of the sensory cortex in other animals and humans, however, deals with real-life stimuli that are statistically complex. Our progress in understanding how cortical circuits represent complex stimuli, such as speech and music, has been limited because standard statistical methods do not work well with complex stimuli. But today the situation has changed. Cutting-edge methods of receptive-field analysis that work well with any kind of natural stimuli and can discover complete representations have been recently developed, and I have tested them successfully in the auditory system of songbirds. We can at last investigate encoding in cortical neurons with stimuli that matter to animals, which will be decisive for understanding how the brain represents complex natural sounds.In this project, I propose to investigate at the single-neuron resolution the principles that govern these representations by neural circuits in auditory cortex. We will use mice, because unlike songbirds, they have auditory cortex. Like songbirds, mice sing to each other melodic songs (at frequencies that are too high for humans to hear). These ultrasonic vocalizations (USVs) form a part of flexible social communication in mice, and neurons in the mouse auditory cortex respond to them.We will address the following questions. 1) Does an individual neuron respond to several (many) different features of natural stimuli, or only to a single one? In other words, what is a neuron's receptive field? Computational models indicate that neuronal ensembles composed of diverse, mosaic receptive fields, are superior for encoding complex stimuli compared to populations in which each neuron responds only to a single stimulus feature. 2) What are the rules that govern how features are combined within a receptive field to achieve robust representations resistant to noise?To answer these questions, we will record responses of excitatory and inhibitory neurons to behaviourally relevant USVs in different layers of the auditory cortex sensitive to vocalizations. For comparison, we will also record responses to unfamiliar, behaviourally irrelevant birdsongs. We will then use the new statistical methods to compute the neurons' receptive fields.Next, we will take advantage of the latest advances in the field of machine learning and train state-of-the-art unsupervised neural networks to discover statistically optimal representations of these complex sounds. We will then compare these representations to those found in vivo. I expect that the artificial and natural representations of USVs (but not birdsongs) will involve the same or similar features, indicating that the brain represents vocalizations in a statistically optimal way.Finally, having identified features that drive individual cortical neurons, we will characterize whether they are combined within a receptive field in a way that helps to achieve representations that are resistant to acoustical noise-a fundamental property of animal and human hearing.The proposed work will advance our understanding of how the brain encodes natural sounds. The availability of the new statistical methods means that we can solve this long-standing problem now. Because USV communication is impaired in murine models of brain disorders accompanied by perturbed central auditory processing, such as autism spectrum disorders, understanding neuronal and computational mechanisms of central auditory processing in the mouse will help us understand both normal hearing and auditory and communication deficits in humans.

感觉系统如何表征自然信号是神经科学中一个长期存在的经典问题。使用与动物相关的刺激产生了对感觉神经元如何在“特殊动物”中工作的最清晰的描述，如蝙蝠，电鱼和谷仓猫头鹰。在每一个例子中，刺激都是自然而简单的，这是理解它们的表征的关键。然而，其他动物和人类的大部分感觉皮层处理的是统计上复杂的现实刺激。我们在理解皮层回路如何代表复杂刺激（如语音和音乐）方面的进展有限，因为标准的统计方法不能很好地处理复杂刺激。但今天情况发生了变化。最近开发出了尖端的感受场分析方法，这些方法可以很好地处理任何类型的自然刺激，并且可以发现完整的表征，我已经在鸣禽的听觉系统中成功地测试了它们。我们终于可以研究皮层神经元对动物重要刺激的编码，这将是理解大脑如何表征复杂自然声音的决定性因素。在这个项目中，我建议在单神经元分辨率上研究听觉皮层神经回路控制这些表征的原理。我们将使用老鼠，因为与鸣禽不同，它们有听觉皮层。像鸣禽一样，老鼠也会互相唱出旋律优美的歌曲（频率太高，人类听不到）。这些超声波发声（USV）形成了小鼠灵活的社会交流的一部分，小鼠听觉皮层的神经元对它们做出反应。1)单个神经元是对自然刺激的几个（许多）不同特征作出反应，还是只对一个特征作出反应？换句话说，什么是神经元的感受野？计算模型表明，神经元集合组成的多样性，马赛克感受野，是上级的编码复杂的刺激相比，其中每个神经元只响应于一个单一的刺激功能的人口。2)在感受野中，是什么规则决定了特征是如何组合的，以实现抗噪声的鲁棒表征？为了回答这些问题，我们将记录兴奋性和抑制性神经元对发声敏感的听觉皮层不同层中行为相关USV的反应。为了进行比较，我们还将记录对不熟悉的、与行为无关的鸟鸣的反应。接下来，我们将利用机器学习领域的最新进展，训练最先进的无监督神经网络，以发现这些复杂声音的统计最优表示。然后，我们将这些表征与体内发现的表征进行比较。我希望无人艇的人工和自然表现（但不是鸟鸣）将涉及相同或相似的特征，这表明大脑以统计学上最优的方式表示发声。最后，已经确定了驱动单个皮层神经元的特征，我们将表征它们是否以有助于实现抗声学噪音的表示的方式组合在感受野内-这是动物和人类听觉的一个基本特性。这项工作将促进我们对大脑如何编码自然声音的理解。新的统计方法的可用性意味着我们现在可以解决这个长期存在的问题。由于USV通信在伴有中枢听觉处理干扰的脑疾病小鼠模型中受损，例如自闭症谱系障碍，因此了解小鼠中枢听觉处理的神经元和计算机制将有助于我们了解人类的正常听力和听觉和通信缺陷。