The computational importance of cerebellar processing

小脑处理的计算重要性

基本信息

批准号：
9040945
负责人：
Emre R Aksay
金额：
$ 55.25万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2018-03-31
项目状态：
已结题

项目摘要

ABSTRACT Cerebellar processing is associated with the accurate performance of a range of behaviors, from sensorimotor transformations to executive control. Given this wide range, there is remarkable consistency across modality and species in the organization of cerebellar microcircuitry and the closed-loop manner with which cerebellar regions are connected to other brain areas. This consistency suggests a common computational role, which we hypothesize is most generally described as an adaptive temporal filter. To test this hypothesis, we will investigate cerebellar function in a simple motor plasticity, the learning of fixation stability. In this setting, processing as an adaptive filter should be realized as a capacity in the cerebellum to alternatively act as a proportional, integrating, or differentiating gain element. In Aim 1, cerebellar filtering will be assessed by using two-photon calcium imaging in the larval zebrafish to measure activity at both input granule and output Purkinje cells populations. Adaptation of the filter will be determined by measuring changes in the relationship between input and output neurons as fixations are trained toward greater or lesser stability. In Aim 2, computational models of the cerebellum will be constructed that generate the experimentally measured signal transformation and make predictions about the mechanisms of cerebellar filtering. These predictions will be tested by focal stimulation of granule cells and measurement of resultant Purkinje neuron responses. Together these data promise to generate the most complete understanding to date of the cerebellum's computational importance in behavior.

抽象的小脑加工与一系列范围的准确性能有关行为，从感觉运动转换到执行控制。给定这个宽范围，组织中的模式和物种具有显着的一致性小脑微电路和小脑区域的闭环方式连接到其他大脑区域。这种一致性表明了一个常见的计算角色，我们假设的角色通常被描述为一种自适应时间过滤器。到检验此假设，我们将研究简单的运动可塑性中的小脑功能，学习固定稳定性。在这种情况下，作为自适应过滤器的处理应为被视为小脑的能力，可以作为比例，整合，或区分增益元素。在AIM 1中，将通过使用小脑过滤幼虫斑马鱼中的两光子钙成像，以测量两种输入的活性颗粒和输出Purkinje细胞种群。将确定过滤器的改编通过测量输入和输出神经元之间关系的变化作为固定接受更大或更少的稳定性训练。在AIM 2中，计算模型将构建小脑，生成实验测量的信号转换并预测小脑滤波的机制。这些将通过对颗粒细胞的局灶性刺激和测量的测量来测试预测由此产生的浦肯野神经元反应。这些数据共同有望产生最多小脑的计算重要性的完全理解行为。