Melssen W J, Epping W J
Department of Medical Physics and Biophysics, University of Nijmegen, The Netherlands.
Biol Cybern. 1987;57(6):403-14. doi: 10.1007/BF00354985.
Crosscorrelation analysis of simultaneously recorded activity of pairs of neurons is a common tool to infer functional neural connectivity. The adequacy of crosscorrelation procedures to detect and estimate neural connectivity has been investigated by means of computer simulations of small networks composed of fairly realistic modelneurons. If the mean interval of neural firings is much larger than the duration of postsynaptic potentials, which will be the case in many central brain areas excitatory connections are easier to detect than inhibitory ones. On the other hand, inhibitory connections are revealed better if the mean firing interval is much smaller than post-synaptic potential duration. In general the effects of external stimuli and the effects of neural connectivity do not add linearly. Furthermore, neurons may exhibit a certain degree of timelock to the stimulus. For these reasons the commonly applied "shift predictor" procedure to separate stimulus and neural effects appears to be of limited value. In case of parallel direct and indirect neural pathways between two neurons crosscorrelation analysis does not estimate the direct connection but instead an effective connectivity, which reflects the combined influences of the parallel pathways.
对同时记录的神经元对活动进行互相关分析是推断功能性神经连接的常用工具。通过由相当逼真的模型神经元组成的小网络的计算机模拟，研究了互相关程序检测和估计神经连接的充分性。如果神经放电的平均间隔远大于突触后电位的持续时间，在许多中枢脑区情况就是如此，那么兴奋性连接比抑制性连接更容易检测到。另一方面，如果平均放电间隔远小于突触后电位持续时间，抑制性连接会更明显地显示出来。一般来说，外部刺激的影响和神经连接的影响不会线性相加。此外，神经元可能对刺激表现出一定程度的时间锁定。由于这些原因，常用的用于分离刺激和神经影响的“移位预测器”程序似乎价值有限。在两个神经元之间存在平行的直接和间接神经通路的情况下，互相关分析估计的不是直接连接，而是一种有效连接，它反映了平行通路的综合影响。
