Mechanisms for Internal Models in a Cerebellum-like Circuit

项目概要/摘要 预测行动的后果是神经系统的重要功能。假设的神经元 基板是所谓的内部模型，它转换有关传出电机命令和 当前的感觉状态转化为感觉输入的预测。这种内部模型可能对于广泛的领域至关重要 感觉、运动和认知功能及其破坏与神经系统疾病有关 例如自闭症和精神分裂症。然而，事实证明，理解内部模型如何 在哺乳动物大脑的神经回路中实现。我们之前的研究成功地开发了 详细了解斑尾鱼电感觉叶 (ELL) 神经元如何预测 并消除一个简单行为的感官后果——器官放电（EOD）脉冲。 然而，由于这些研究是在固定的动物中进行的，因此研究的预测的性质 范围和复杂性都受到限制。这次更新使用了神经记录和高分辨率的新方法 监测自由游动的鱼类的行为，以研究其背后更复杂的内部模型 电鱼显着的主动电定位能力。将使用计算建模方法 既严格定义主动电传感系统面临的问题，又生成和测试现实的 如何解决这些问题的电路级模型。此类模型的关键组成部分，包括突触 可塑性、循环和前馈连接以及轴突和树突的生物物理分区 尖峰是许多神经系统所共有的，包括小脑、海马体和新皮质。因此 这些研究的见解预计将与理解内部模型的运作方式广泛相关 在神经系统中实现。

Project Summary/Abstract Predicting the consequences of action is a vital function of the nervous system. The hypothesized neural substrate are so-called internal models that transform information about outgoing motor commands and the current sensory state into predictions of sensory input. Such internal models are likely critical for a wide range of sensory, motor, and cognitive functions and their disruption has been implicated in neurological disorders such as autism and schizophrenia. Nevertheless, it has proven challenging to understand how internal models are implemented in neural circuits in the mammalian brain. Our prior studies were successful in developing a detailed mechanistic understanding of how neurons in the electrosensory lobe (ELL) of mormyrid fish predict and cancel out the sensory consequences of a simple behavior--the electric organ discharge (EOD) pulse. However, because these studies were performed in immobilized animals, the nature of the predictions studied was limited in scope and complexity. This renewal uses novel methods for neural recording and high-resolution behavior monitoring in freely swimming fish to study the more complex internal models underlying the remarkable active electrolocation abilities of electric fish. Computational modeling approaches will be used both to rigorously define the problem facing the active electrosensory system and to generate and test realistic circuit-level models of how they may be solved. The key components of such models, including synaptic plasticity, recurrent and feedforward connectivity, and biophysical compartmentalization of axonal and dendritic spikes, are common to many neural systems including the cerebellum, hippocampus, and neocortex. Hence insights from these studies are expected to be widely relevant to understanding how internal models are implemented in neural systems.