Collaborative Research: Effective Connectivity and Computations in Local Cortical Networks

合作研究：局部皮质网络的有效连接和计算

• 批准号: 0904413
• 负责人: Alan Litke
• 金额: $ 27.3万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904413&HistoricalAwards=false
• 关键词: Collaborative; Research; Effective; Connectivity; Computations

A central task in understanding how neurons collectively process information is to map how neurons influence each other in local cortical networks. As defined here, local cortical networks will consist of tens to hundreds of neurons. Influence will be defined as how well knowledge of activity in one neuron will allow the activity in another neuron to be predicted. Three methods for measuring influence between neurons will be explored. To assess these methods, they will be used on data from simple, and then realistic, models of cortical networks where the underlying connectivity structure is known. After refinement, the methods will be applied to recordings from hundreds of cortical neurons in small slice cultures of brain tissue. Over 100 cortical neurons at a time will be recorded through the use of an advanced, 512 electrode array. In addition, measures of influence will be applied to data taken from 16 wire electrodes placed in behaving rats. These in vivo recordings will serve as a first step toward linking influence maps in cortical networks to behavior. This research is expected to provide new knowledge that could aid the design of brain-like computing devices. In addition, it could ultimately be used as a tool to identify differences in influence patterns between healthy and pathological brains. The three methods for measuring influence will include directed information, transfer entropy, and Granger causality. Special care will be taken to identify situations where these measurements may produce false positive connections. These include cases where two neurons are driven by a common source at different delays, and cases where one neuron influences another neuron indirectly through an intercalated neuron. Such false positive connections will be identified and corrected, to the extent possible, by comparing raw pairwise measures of influence with conditional measures of influence. Simulations will also provide an estimate of how often neurons outside the recorded population can contribute to false positive connections. These estimates will be used to place confidence limits on the influence maps extracted from actual data. In neurons where influences converge, synergistic interactions between influences will be measured. The map of influence will serve to identify locations within the network where synergistic transformations of information, or computations, occur.

理解神经元如何共同处理信息的核心任务是映射神经元如何在局部皮质网络中相互影响。如这里定义的那样，局部皮质网络将包括数十个神经元的神经元。影响将被定义为一个神经元中对活动的知识如何允许预测另一个神经元中的活动。将探索三种测量神经元之间影响的方法。为了评估这些方法，它们将用于来自简单的数据，然后是现实的皮质网络模型，这些模型已知基础连接结构。细化后，这些方法将应用于脑组织小切片培养物中数百个皮质神经元的记录。一次将通过使用高级的512电极阵列记录100多个皮质神经元。此外，影响的度量将应用于从行为大鼠中的16个电线电极中获取的数据。这些体内记录将是将皮质网络中影响图的影响与行为联系起来的第一步。这项研究有望提供新知识，可以帮助设计类似大脑的计算设备。此外，它最终可以用作确定健康和病理大脑之间影响模式差异的工具。测量影响的三种方法将包括定向信息，转移熵和Granger因果关系。将要特别注意确定这些测量结果可能会产生假积极连接的情况。这些情况包括在不同延迟下由共同来源驱动两个神经元的情况，以及一个神经元通过插入的神经元间接影响另一个神经元的情况。通过比较原始的成对措施与条件措施的影响力，将在可能的范围内确定和纠正这种假阳性连接。模拟还将提供对记录人群以外的神经元的频率估计可以导致假阳性联系的频率。这些估计将用于对从实际数据提取的影响图提出置信度限制。在影响融合的神经元中，将测量影响之间的协同相互作用。影响力图将有助于确定网络中信息或计算的协同转换的位置。