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Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs

机译:缓慢适应的拉伸受体器官的神经峰值训练中的复杂性度量

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摘要

Discrete sequence analysis methods were applied to study spike-trains generated by the isolated neuron of the slowly adapting stretch receptor organ. Calculation of the algorithmic complexity and block entropies of digitized individual spike-train forms allowed us to distinguish different classes of neural behavior. While some spike-trains exhibited significant structure, others displayed diverse degrees of randomness. The sequences recorded during the stimulated portions of the intermittent and walk-through forms, differed considerably from their randomly shuffled surrogates. Informational and grammar complexity measures (in two, four and eight-letter alphabets), tell us things about the structure of spike-trains that are not obtained, with conventional spike analysis. Comparison of the conditional entropies for the digitized signals showed that the method distinguishes between different stimulated conditions. Additionally, comparison of the different stimulated conditions with their corresponding surrogates showed that, both, conditional entropies and complexities were significantly different for the two groups. Although the original and the randomly shuffled sequences had the same distribution and average firing rate, their complexity values were different. The results obtained with both measures of sequence structure were quite consistent (C) 2000 Elsevier Science Ireland Ltd. All rights reserved. [References: 15]
机译:离散序列分析方法用于研究由慢适应性拉伸受体器官的分离神经元产生的刺突。数字化单个峰值训练形式的算法复杂度和块熵的计算使我们能够区分不同类别的神经行为。尽管一些峰值序列显示出显着的结构,但另一些序列则显示出不同程度的随机性。在间歇和遍历形式的受激部分中记录的序列与它们随机改组的替代物有很大不同。信息和语法复杂性度量(用两个,四个和八个字母组成的字母)告诉我们有关使用常规峰值分析无法获得的峰值序列结构的信息。数字信号的条件熵比较表明,该方法可以区分不同的刺激条件。另外,将不同刺激条件与其相应的替代物进行比较表明,两组的条件熵和复杂性均显着不同。尽管原始序列和随机重组的序列具有相同的分布和平均发射速率,但是它们的复杂度值却不同。用两种序列结构的度量获得的结果都非常一致(C)2000 Elsevier Science Ireland Ltd.保留所有权利。 [参考:15]

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