le Feber J, Rutten W L C, Stegenga J, Wolters P S, Ramakers G J A, van Pelt J
Biomedical Signals and Systems/Department of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500AE Enschede, The Netherlands.
J Neural Eng. 2007 Jun;4(2):54-67. doi: 10.1088/1741-2560/4/2/006. Epub 2007 Feb 28.
To properly observe induced connectivity changes after training sessions, one needs a network model that describes individual relationships in sufficient detail to enable observation of induced changes and yet reveals some kind of stability in these relationships. We analyzed spontaneous firing activity in dissociated rat cortical networks cultured on multi-electrode arrays by means of the conditional firing probability. For all pairs (i, j) of the 60 electrodes, we calculated conditional firing probability (CFP(i,j)[tau]) as the probability of an action potential at electrode j at t = tau, given that one was detected at electrode i at t = 0. If a CFP(i,j)[tau] distribution clearly deviated from a flat one, electrodes i and j were considered to be related. For all related electrode pairs, a function was fitted to the CFP-curve to obtain parameters for 'strength' and 'delay' (i.e. maximum and latency of the maximum of the curve) of each relationship. In young cultures the set of identified relationships changed rather quickly. At 16 days in vitro (DIV) 50% of the set changed within 2 days. Beyond 25 DIV this set stabilized: during a week more than 50% of the set remained intact. Most individual relationships developed rather gradually. Moreover, beyond 25 DIV relational strength appeared quite stable, with coefficients of variation (100 x SD/mean) around 25% in periods of approximately 10 h. CFP analysis provides a robust method to describe the underlying probabilistic structure of highly varying spontaneous activity in cultured cortical networks. It may offer a suitable basis for plasticity studies, in the case of changes in the probabilistic structure. CFP analysis monitors all pairs of electrodes instead of just a selected one. Still, it is likely to describe the network in sufficient detail to detect subtle changes in individual relationships.
为了在训练后正确观察诱导的连通性变化，需要一个网络模型，该模型能足够详细地描述个体间的关系，以便能够观察到诱导变化，同时在这些关系中呈现出某种稳定性。我们通过条件发放概率分析了在多电极阵列上培养的离体大鼠皮层网络中的自发放电活动。对于60个电极的所有电极对 (i, j)，我们计算条件发放概率 (CFP(i,j)[tau])，即假设在t = 0时电极i检测到一个动作电位，那么在t = tau时电极j产生动作电位的概率。如果CFP(i,j)[tau]分布明显偏离均匀分布，则电极i和j被认为是相关的。对于所有相关电极对，对CFP曲线拟合一个函数，以获得每个关系的“强度”和“延迟”参数（即曲线最大值和最大值的潜伏期）。在年轻培养物中，识别出的关系集变化相当快。在体外培养16天 (DIV) 时，该集合的50%在2天内发生变化。超过25 DIV后，该集合趋于稳定：在一周内，超过50%的集合保持不变。大多数个体关系发展较为缓慢。此外，超过25 DIV后，关系强度似乎相当稳定，在大约10小时的时间段内，变异系数（100 x SD/均值）约为25%。CFP分析提供了一种强大的方法来描述培养的皮层网络中高度变化的自发放电活动的潜在概率结构。在概率结构发生变化的情况下，它可能为可塑性研究提供合适的基础。CFP分析监测所有电极对，而不仅仅是选定的一个电极对。尽管如此，它仍可能足够详细地描述网络，以检测个体关系中的细微变化。
