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

理解神经元如何集体处理信息的一个中心任务是绘制神经元如何在局部皮层网络中相互影响。正如这里所定义的，局部皮层网络将由数十到数百个神经元组成。影响力将被定义为一个神经元的活动知识将允许预测另一个神经元的活动。将探讨三种测量神经元之间影响的方法。为了评估这些方法，他们将使用简单的数据，然后现实的，皮层网络模型的基础连接结构是已知的。经过改进后，这些方法将被应用于脑组织小切片培养物中数百个皮层神经元的记录。通过使用先进的512电极阵列，一次记录超过100个皮层神经元。此外，将对放置在行为大鼠中的16根电极丝采集的数据进行影响测量。这些活体记录将成为将大脑皮层网络中的影响地图与行为联系起来的第一步。这项研究有望提供新的知识，帮助设计类似大脑的计算设备。此外，它最终可以作为一种工具来识别健康和病理大脑之间的影响模式差异。衡量影响力的三种方法包括定向信息、转移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。这些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嵌入神经元间接影响另一个神经元的情况。将通过比较原始成对影响测量值与条件影响测量值，尽可能识别和纠正此类假阳性连接。模拟还将提供对记录群体之外的神经元促成假阳性连接的频率的估计。这些估计值将用于确定从实际数据中提取的影响图的置信限。在影响汇聚的神经元中，将测量影响之间的协同相互作用。影响图将用于确定网络中发生信息或计算的协同转换的位置。

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.