Collaborative Research: Causal Connectivity and Computations in Hundreds of Neurons in Cortex

了解神经元如何集体处理信息的核心任务是绘制神经元如何在局部皮质网络中相互影响。根据这里的定义，局部皮层网络将由数十到数百个神经元组成。影响将被定义为对一个神经元活动的了解程度，将允许预测另一个神经元的活动。我们将探讨测量神经元之间影响的三种方法。为了评估这些方法，他们将使用简单的数据，然后是现实的皮质网络模型，其中潜在的连接结构是已知的。经过改进后，这些方法将应用于脑组织小切片培养中数百个皮质神经元的记录。通过使用先进的512个电极阵列，一次将记录100多个皮质神经元。此外，将对从放置在行为正常的大鼠身上的16个导线电极采集的数据进行影响测量。这些体内记录将作为将皮质网络中的影响图与行为联系起来的第一步。这项研究有望提供有助于设计类脑计算设备的新知识。此外，它最终可能被用作识别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包括定向信息、传递熵和格兰杰因果关系。将特别注意识别这些测量可能产生假阳性连接的情况。这包括两个神经元由一个共同的源以不同的延迟驱动的情况，以及一个神经元通过插入神经元间接影响另一个神经元的情况。将通过比较原始的两两影响措施与有条件的影响措施，尽可能地识别和纠正这种假阳性联系。模拟还将提供对记录人群之外的神经元产生假阳性连接的频率的估计。这些估计值将用于对从实际数据中提取的影响图设定置信限。在影响汇聚的神经元中，将测量影响之间的协同相互作用。影响地图将有助于确定网络内信息或计算发生协同转变的地点。

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.