A Neuroethological Approach to Understanding Cerebellar Function

神经科学方面的许多突破都来自对“冠军”动物物种的研究。例如，我们对听觉的了解大多来自对猫头鹰和蝙蝠的研究，这些动物的生存依赖于在黑暗中捕捉食物。有关神经元之间相互交流的电信号的基本知识来自对鱿鱼的研究，在这种情况下，电信号通过特别大的“电线”传递，从而能够做出快速反应，帮助鱿鱼避免成为食物(这使得神经科学家很容易进行研究)。在这里，PI将应用这一简单的逻辑来理解大脑的一个区域，这个区域长期以来一直让神经科学家着迷，但其功能仍然是个谜。虽然小脑的体积相对较小，但它包含了人类大脑中约3/4的神经元。此外，小脑疾病会导致严重的运动、情感和思维障碍，包括自闭症。开发治疗方法需要更好地了解小脑的正常功能。为了实现这一目标，该项目提议研究一种动物的小脑，在这种动物中，小脑是最大和最发达的动物--一种来自非洲的鱼类，被称为桑鱼。小脑如此之大，以至于它们的大脑(就它们的身体大小而言)甚至比人类的大脑还要大。这将帮助他们建立小脑如何工作的模型，直到其微观结构的细节。由于这种微观结构在动物中极其相似，我们的工作可能会揭示直接适用于人类的基本原理。尽管小脑的晶体电路长期以来一直鼓励人们努力将神经电路结构和功能联系起来，但我们对其功能的理解仍然局限于少数几个特定的案例，例如经典的反射条件反射。这个项目通过将综合的实验和计算方法应用于脊椎动物群体的研究来应对这一挑战，脊椎动物群体具有比例最大和最发达的小脑--弱电鱼。对一种常见的变态动物物种彼得象鼻鱼的初步研究发现，小脑中有一个区域被称为c1，专门控制手指状的下巴附件，或称Schnauznowgan，这些鱼就是以这个名字命名的。高流动性的Schnauznowgan被密集的电子感受器覆盖，对该物种的觅食行为至关重要。值得注意的是，C1输出神经元直接投射到支配纹状体肌肉并控制其运动的脑干运动神经元。这一简洁的回路与哺乳动物小脑对运动或伸展等通常研究的行为有贡献的复杂和高度分布的路径形成了对比。由于该系统的固有优势和生态有效性，再加上紧密集成的实验和计算建模方法，预计将取得快速进展。在实验方面，PI将利用新的方法进行高分辨率的行为分析，并在觅食过程中对自由游动的鱼类进行大规模神经记录。机器学习工具、神经电路模型和生物力学模型将使我们能够识别Schnauznowgan的感觉运动控制所涉及的计算问题，并开发和测试有关如何解决这些问题的假设。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many breakthroughs in neuroscience have come from studies of “champion” animal species. For example, much of what we know about hearing has come from studies of owls and bats, animals whose survival depends on capturing their meals in darkness. Fundamental knowledge about the electrical signals through which neurons communicate with one another came from studies of squid, where electric signals travel through “wires” that are particularly large, enabling fast reactions that help the squid to avoid becoming a meal (and making them easy for neuroscientists to study). Here, the PI will apply this straightforward logic to understand a region of the brain that has long fascinated neuroscientists but whose function remains mysterious. Though relatively small in size, the cerebellum contains ~3/4 of all the neurons in the human brain. Moreover, diseases of the cerebellum cause profound disorders of movement as well as emotion and thought, including autism. Developing treatments requires a better understanding of the normal functioning of the cerebellum. To achieve this, the project proposes to study the cerebellum in the animal in which it the largest and most highly-developed—a group of fish from Africa known as the mormyrids. The cerebellum is so large in mormyrids that their brain is even larger (for their body size) than the human brain. This will help them to build models of how the cerebellum works, down to the details of its microscopic structure. Because this microscopic structure is extremely similar across animals, our work may reveal fundamental principles that apply directly to humans.Though the crystalline circuitry of the cerebellum has long inspired efforts to link neural circuit structure and function, our understanding of its function remains restricted to a few select cases, such as classical reflex conditioning. This projects addresses this challenge by applying integrated experimental and computational approaches to studies of the vertebrate group with the proportionately largest and most highly developed cerebellum--weakly electric mormyrid fish. Preliminary studies of a common mormyrid species, Peter’s elephant-nose fish, have identified a region of the cerebellum, known as C1, dedicated to controlling the finger-like chin appendage, or schnauzenorgan, for which these fish are named. The highly-mobile schnauzenorgan is densely covered with electroreceptors and is vital for the foraging behavior of this species. Remarkably, C1 output neurons project directly to the brainstem motor neurons that innervate schnauzenorgan muscles and control its movement. This concise circuitry contrasts with the complex and highly distributed paths via which the mammalian cerebellum contributes to commonly studied behaviors such as locomotion or reaching. Rapid progress is expected due to the inherent advantages and ecological validity of this system coupled with the tightly integrated experimental and computational modeling approaches. On the experimental side, the PI will leverage new methods for high-resolution behavioral analysis and large-scale neural recordings in freely swimming fish during foraging. Machine learning tools, neural circuit models, and biomechanical models will allow us to identify the computational problems involved in sensorimotor control of the schnauzenorgan and to develop and test hypotheses regarding how they may be solved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.