The computational importance of cerebellar processing
The computational importance of cerebellar processing
批准号:
8238779
负责人:
Emre R Aksay
金额:
$58.65万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-04-01 至 2017-03-31
关键词:
AccelerationAddressAgonistArchitectureAreaAutistic DisorderBehaviorBrainCalciumCalibrationCellsCerebellar AtaxiaCerebellar DiseasesCerebellumComputer SimulationCoupledCytoplasmic GranulesDataDiseaseDyslexiaElementsEyeGlutamatesImageLearningMeasurementMeasuresMediatingModalityModelingMotionMotorMotor ActivityMovementNeuronsOutputPathologic NystagmusPerformancePopulationPositioning AttributePreparationProceduresProcessPurkinje CellsRelative (related person)RetinaRetinalRoleSensorySignal TransductionSiteSmooth PursuitSystemTestingTimeTrainingUncertaintyUrsidae FamilyVisionVisual AcuityWorkZebrafishawakebasecell motilityexecutive functionfallsgazegranule cellimprovedoculomotoroculomotor behavioroperationresearch studyresponsesample fixationtwo-photonvisual motor
中文摘要
描述(由申请人提供):小脑处理与一系列行为的准确表现有关,从感觉运动转换到执行控制。鉴于这种广泛的范围,在小脑微电路的组织和小脑区域与其他大脑区域连接的闭环方式方面,不同形态和物种之间存在显着的一致性。这种一致性表明一个共同的计算作用,我们假设是最普遍的描述为自适应时间滤波器。为了验证这一假设,我们将研究小脑功能在一个简单的运动可塑性,学习的固定稳定性。在这种情况下,作为自适应滤波器的处理应该被实现为小脑中的一种能力,以交替地充当比例、积分或微分增益元件。在目标1中,将通过在幼斑马鱼中使用双光子钙成像来评估小脑过滤,以测量输入颗粒和输出浦肯野细胞群体的活性。滤波器的自适应将通过测量输入和输出神经元之间的关系的变化来确定,因为固定被训练成更大或更小的稳定性。在目标2中,将构建小脑的计算模型,以生成实验测量的信号转换,并对小脑滤波的机制进行预测。这些预测将通过颗粒细胞的局灶性刺激和由此产生的浦肯野神经元反应的测量进行测试。这些数据有望使我们对小脑在行为中的计算重要性有一个最全面的了解。
公共卫生相关性:小脑包含了大脑中几乎一半的神经元,但我们并不清楚小脑的功能。在这个项目中,我们将通过记录大多数小脑神经元在行为过程中的信息来确定小脑是如何处理的。这些结果将为开发小脑功能疾病的治疗方法奠定必要的基础,包括脊髓小脑共济失调,阅读障碍和自闭症。
英文摘要
DESCRIPTION (provided by applicant): 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.
PUBLIC HEALTH RELEVANCE: The cerebellum contains almost half of all neurons in the brain, yet we don't have clear understanding what the cerebellum does. In this project we will determine how information is processed in the cerebellum by recording from the majority of cerebellar neurons during behavior. These results will lay the groundwork necessary to develop treatments for diseases of cerebellar function, including spino-cerebellar ataxia, dyslexia, and autism.
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会议论文
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海外基金