Roles for Granule Cells in Adaptive Processing in a Cerebellum-like Circuit

描述(由申请人提供)：预测允许知识和经验指导行动，对一系列感觉、运动和认知功能至关重要。未能产生准确的预测可能会导致自闭症和精神分裂症等神经疾病。这项提议利用了一个有利的模型系统--弱电鱼类--这将使我们能够剖析预测感觉事件的细胞和电路机制。电鱼在它们的皮肤上有特殊的感受器，使它们能够探测到水中其他动物发出的微弱电场。这种电感使它们能够避开捕食者，并在黑暗中找到猎物。然而，这些鱼也会产生它们自己的电场。因此，电感觉系统面临的一个挑战是区分由外部事件引起的与行为相关的电感觉输入模式和那些由自身产生的模式。尽管在电感觉系统中的研究特别清楚和容易，但所有感觉系统都面临着同样的问题。一个多世纪以来，科学家和哲学家一直对我们如何感知一个稳定的视觉世界感到困惑，尽管由于眼睛的快速运动，视觉输入每秒会发生几次戏剧性的变化。一个可能的答案是，大脑对我们自己的运动导致的视觉输入的变化产生预测，并从实际的感觉输入中减去这些预测。此前的研究表明，这一过程发生在电鱼大脑中与小脑非常相似的区域。以前的研究已经能够直接证明，预测是通过神经元之间联系强度的变化形成的，这一过程被称为突触可塑性。类似的突触可塑性机制存在于哺乳动物的大脑皮层和小脑中，被认为是学习和记忆的基础。这一建议使用神经记录和计算建模来检验这样一个假设，即小脑颗粒细胞产生过去时间的表示，这对于产生关于传入电感觉输入的时间模式的准确预测至关重要。尽管开创性的理论在40多年前的哺乳动物小脑依赖运动学习的背景下提出了颗粒细胞的类似功能，但仍然缺乏直接的实验支持。这些拟议的研究将为小脑回路的功能、时间信息的神经表示以及预测感觉事件的神经机制提供新的见解。 公共卫生相关性：预测或预测感官事件的能力对于准确的感知、协调的运动和正常的认知功能至关重要。尽管预测能力受损与自闭症和精神分裂症等神经系统疾病有关，但人们对其基本神经机制知之甚少。这一提议利用一个独特的模型系统来直接洞察预测感觉事件的细胞和电路机制，因此代表着理解这些复杂过程的破坏如何导致疾病的关键一步。

DESCRIPTION (provided by applicant): Prediction allows knowledge and experience to guide action and is critical for a range of sensory, motor, and cognitive functions. Failure to generate accurate predictions could contribute to neurological disorders such as autism and schizophrenia. This proposal takes advantage of an advantageous model system--a weakly electric fish--that will allow us to dissect the cellular and circuit mechanisms for predicting sensory events. Electric fish possess special receptors on their skin that allow them to detect weak electrical fields emitted by other animals in the water. This electrosense allows them to avoid predators and find prey in darkness. However, these fish also generate electrical fields of their own. Hence, a challenge for the electrosensory system is to distinguish between behaviorally relevant patterns of electrosensory input due to external events from those that are self-generated. Though particularly clear and accessible to study in electrosensory systems, this same problem faces all sensory systems. For over a century scientists and philosophers have puzzled over how we perceive a stable visual world despite the fact that visual input changes dramatically several times per second due to rapid movements of the eyes. One possible answer is that the brain generates predictions about changes in visual input that will result from our own movements and subtracts these predictions from the actual sensory input. Previous studies have shown that just such a process occurs in a region of the brain of electric fish that closely resembles the cerebellum. Previous studies have been able to directly demonstrate that predictions are formed via changes in the strength of connections between neurons, a process known as synaptic plasticity. Similar synaptic plasticity mechanisms exist in the mammalian cerebral cortex and cerebellum and are believed to underlie learning and memory. This proposal uses neural recordings and computational modeling to test the hypothesis that cerebellar granule cells generate representations of elapsed time that are critical for generating accurate predictions about temporal patterns of incoming electrosensory input. Though seminal theories proposed similar functions for granule cells in the context of cerebellar-dependent motor learning in mammals over 40 years ago, direct experimental support is still lacking. The proposed studies will provide novel insights into functions of cerebellar circuitry, neural representations of temporal information, and the neural mechanisms for predicting sensory events. PUBLIC HEALTH RELEVANCE: The ability to anticipate or predict sensory events is critical for accurate perceptions, coordinated movements, and normal cognitive function. Though impaired predictive capacities have been implicated in nervous system disorders such as autism and schizophrenia, very little is known about their basic neural mechanisms. This proposal takes advantage of a unique model system to gain direct insights into the cellular and circuit mechanisms for predicting sensory events, and hence represents a critical step towards understanding how disruption of these complex processes contributes to disease.