Neural mechanisms for sensory prediction in a cerebellum-like structure

描述(由申请人提供)： 预测允许知识和经验来指导行动，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。这一建议利用了一个简单的模型系统，在该系统中，可以剖析细胞和电路机制以生成预测并了解它们的功能角色。弱电鱼类在尾巴上有一个产生电场的特殊器官，以及对电场强度的微小变化敏感的特殊电感受器。探测到附近物体引起的视野变化，使鱼能够在黑暗中导航并找到猎物。然而，由于电器官位于尾部，而电感受器位于头部和躯干，鱼自身的运动也会改变电输入的模式。因此，电感觉系统面临的挑战是区分由外部事件引起的与行为相关的输入模式和自我产生的模式。尽管在电感觉系统中的研究特别清楚和容易，但在任何运动的动物身上，同样的问题都面临着感觉系统。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，大脑对视觉输入的变化产生预测，这些变化将由我们自己的运动产生，并从实际的感觉输入中过滤掉这些预测。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。这种预测是通过改变神经元之间的连接强度来形成的，这一过程被称为突触可塑性。在哺乳动物的大脑皮层和小脑中存在着几乎相同的突触可塑性机制，可能是学习和记忆的基础。这项建议使用神经记录和计算建模来提供对两个一般问题的研究。第一个实验将检验这样一个假设，即小脑颗粒细胞(脊椎动物大脑中数量最多的神经元)提供了形成鱼运动的各个方面与由此产生的传入电感觉输入的可预测模式之间的联系所需的关键原料。其次，拟议的研究将确立感觉和运动信号在感官过滤背景下的预测产生中的作用。在突触可塑性、神经回路和感觉功能之间的详细联系，将提供对小脑功能和预测感觉事件的神经机制的关键见解。 公共卫生相关性： 预测或预测感觉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 a simple model system in which it is possible to dissect the cellular and circuit mechanisms for generating predictions and to understand their functional roles. Weakly electric fish possess a specialized organ in their tail that generates an electrical field and specialized electroreceptors that are sensitive to small changes in the strength of the field. Detecting changes in the field induced by nearby objects allows the fish to navigate and find prey in darkness. However, because the electric organ is in the tail and electroreceptors are located on the head and trunk, the fish's own movements also alter patterns of electrical inputs. Hence, the challenge for the electrosensory system is to distinguish between behaviorally relevant patterns of input due to external events from patterns that are self- generated. Though particularly clear and accessible to study in electrosensory systems, this same problem faces sensory systems in any animal that moves. 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 filters out 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. Such predictions are formed via changes in the strength of connections between neurons, a process known as synaptic plasticity. Virtually identical synaptic plasticity mechanisms exist in the mammalian cerebral cortex and cerebellum and likely underlie learning and memory. This proposal uses neural recordings and computational modeling to provide insith into two general issues. The first will test the hypothesis that cerebellar granule cells (the most numerous neurons in the vertebrate brain) provide critical 'raw material' needed for forming associations (via synaptic plasticity) between aspects of the fish's movements and the resulting predictable patterns of incoming electrosensory input. Second, the proposed studies will establish roles for sensory and motor signals in the generation of predictions in the context of sensory filtering. Detailed links between synaptic plasticity, neural circuitry, and sensory function and will provide key insights into both cerebellar function 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.