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

感觉系统如何代表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，可以最清楚地描述感觉神经元在蝙蝠、电鱼和谷仓猫头鹰等“特殊动物”中的工作方式。在每个例子中，刺激都是自然而简单的，这是理解其表征的关键。然而，其他动物和人类的大多数感觉皮质处理的是统计上复杂的现实生活刺激。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。接受野分析的尖端方法在任何类型的自然刺激下都能很好地工作，并可以发现完整的表征，最近已经开发出来，我已经在鸣鸟的听觉系统中成功地测试了它们。最后，我们可以研究皮质神经元中对动物重要的刺激的编码，这将对理解大脑如何代表复杂的自然声音具有决定性作用。在这个项目中，我提议在单个神经元的分辨率下，研究支配听觉皮质中神经回路这些表征的原理。我们将使用老鼠，因为与鸣禽不同，它们有听觉皮质。就像鸣禽一样，老鼠相互唱歌(频率太高，人类听不到)。这些超声发声(USV)形成了小鼠灵活的社会交流的一部分，小鼠听觉皮质中的神经元对它们做出反应。我们将解决以下问题。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，还是只对单一刺激作出反应？换句话说，神经元的感受场是什么？计算模型表明，与每个神经元只对单一刺激特征做出反应的群体相比，由不同的马赛克感受场组成的神经元集成在编码复杂刺激方面具有更好的优势。2)控制特征如何在感受场中组合以获得抵抗噪声的稳健表征的规则是什么？为了回答这些问题，我们将记录听觉皮质对发声敏感的不同层行为相关USV的兴奋性和抑制性神经元的反应。为了进行比较，我们还将记录下对陌生的、与行为无关的鸟鸣的反应。然后，我们将使用新的统计方法来计算神经元的感受场。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，我们将把这些代表与在体内发现的那些进行比较。我预计，USV(但不是鸟鸣)的人工和自然表征将涉及相同或相似的特征，表明大脑以统计上最优的方式代表发声。最后，在确定了驱动单个皮质神经元的特征后，我们将确定它们是否以一种有助于实现抵抗声学噪声的表征的方式在接受野中组合，这是动物和人类听觉的基本特性。拟议的工作将促进我们对大脑如何编码自然声音的理解。新的统计方法的出现意味着我们现在可以解决这个长期存在的问题。由于在伴有中枢听觉处理紊乱的小鼠模型中，USV通信受到损害，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理解人类的正常听力和听觉和通信缺陷。

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.