PRIMARY OBJECTIVE: To determine the relative uses of neural action potential ('spike') data versus local field potentials (LFPs) for modeling information flow through complex brain networks. HYPOTHESIS: The common use of LFP data, which are continuous and therefore more mathematically suited for spectral information-flow modeling techniques such as Granger causality analysis, can lead to spurious inferences about whether a given brain area 'drives' the spiking in a downstream area. EXPERIMENT: We recorded spikes and LFPs from the forelimb motor cortex (M1) and the magnocellular red nucleus (mRN), which receives axon collaterals from M1 projection cells onto its distal dendrites, but not onto its perisomatic regions, as rats performed a skilled reaching task. RESULTS AND IMPLICATIONS: As predicted, Granger causality analysis on the LFPs-which are mainly composed of vector-summed dendritic currents-produced results that if conventionally interpreted would suggest that the M1 cells drove spike firing in the mRN, whereas analyses of spiking in the two recorded regions revealed no significant correlations. These results suggest that mathematical models of information flow should treat the sampled dendritic activity as more likely to reflect intrinsic dendritic and input-related processing in neural networks, whereas spikes are more likely to provide information about the output of neural network processing.

中文翻译：

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在与任务相关的大规模神经回路中跟踪“驱动程序”与“调制器”信息流。

主要目标：确定神经动作电位（“尖峰”）数据与局部场电位（LFP）的相对用途，用于对通过复杂大脑网络的信息流进行建模。假设：LFP 数据的普遍使用是连续的，因此在数学上更适合于光谱信息流建模技术，如格兰杰因果分析，可能导致关于给定大脑区域是否“驱动”下游区域尖峰的虚假推断. 实验：我们记录了来自前肢运动皮层 (M1) 和大细胞红核 (mRN) 的尖峰和 LFP，它们将来自 M1 投射细胞的轴突侧支细胞接收到其远端树突上，但不接收到其周围区域，因为大鼠进行了熟练的触及任务。结果和意义：正如预测的那样，LFP 的 Granger 因果关系分析（主要由矢量求和树突电流组成）产生的结果，如果按照常规解释，则表明 M1 细胞驱动了 mRN 中的尖峰放电，而对两个记录区域中尖峰的分析显示没有显着相关性. 这些结果表明，信息流的数学模型应该将采样的树突活动视为更可能反映神经网络中内在树突和输入相关的处理，而尖峰更有可能提供有关神经网络处理输出的信息。而对两个记录区域的峰值分析显示没有显着相关性。这些结果表明，信息流的数学模型应该将采样的树突活动视为更可能反映神经网络中内在树突和输入相关的处理，而尖峰更有可能提供有关神经网络处理输出的信息。而对两个记录区域的峰值分析显示没有显着相关性。这些结果表明，信息流的数学模型应该将采样的树突活动视为更可能反映神经网络中内在树突和输入相关的处理，而尖峰更有可能提供有关神经网络处理输出的信息。