Nicol Alister U, Segonds-Pichon Anne, Magnusson Magnus S
Department of Physiology, Development and Neuroscience, University of Cambridge, 307 Huntingdon Road, Cambridge CB3 0JX, UK.
Bioinformatics Department, Babraham Institute, Cambridge CB22 3AT, UK.
J Neurosci Methods. 2015 Jan 15;239:11-7. doi: 10.1016/j.jneumeth.2014.09.016. Epub 2014 Sep 26.
T-pattern analysis is a procedure developed for detecting non-randomly recurring hierarchical and multiordinal real-time sequential patterns (T-patterns).
We have inquired whether such patterns of action potentials (spikes) can be extracted from extracellular activity sampled simultaneously from many neurons across the mitral cell layer of the olfactory bulb (OB). Spikes were sampled from urethane-anaesthetized rats over a 6h recording session, or a period lasting as long as permitted by the physiological condition of the animal. Breathing was recorded to mark peak inhalation and exhalation.
Complex T-patterns of up to ∼20 elements were identified with functional connections often spanning the full extent of the array. A considerable proportion of these sequences incorporated breathing.
In contrast to sequence detection by synfire, the incidence of sequences detected in our real data is very much greater than in the same data when randomized either by shuffling, or an alternative procedure preserving the interval structure of each spike train, and so more conservative. Further, when recordings were terminated before completion of the full recording session, the relative pattern detection in real and randomized data was a strong indicator of physiological condition-in recordings leading up to the preparation becoming physiologically unstable, the number of patterns detected in real data approached that in the randomized data.
We conclude that such sequences are an important physiological property of the neural system studied, and suggest that they may form a basis for encoding sensory information.
T 型模式分析是一种用于检测非随机重复出现的层次化和多序实时序列模式（T 型模式）的程序。
我们探究了是否能从嗅球（OB）二尖瓣细胞层中多个神经元同时采样的细胞外活动中提取动作电位（尖峰）的此类模式。在长达 6 小时的记录期内，或在动物生理状况允许的尽可能长的时间段内，从经乌拉坦麻醉的大鼠身上采样尖峰。记录呼吸以标记吸气和呼气峰值。
识别出了多达约 20 个元素的复杂 T 型模式，其功能连接通常跨越阵列的整个范围。这些序列中有相当一部分包含呼吸。
与通过同步激发进行序列检测不同，在我们的实际数据中检测到的序列发生率比通过洗牌或保留每个尖峰序列间隔结构的替代程序进行随机化处理后的相同数据中的发生率要高得多，因此更为保守。此外，当记录在完整记录期完成前终止时，实际数据和随机化数据中的相对模式检测是生理状况的有力指标——在导致标本生理不稳定的记录中，实际数据中检测到的模式数量接近随机化数据中的数量。
我们得出结论，此类序列是所研究神经系统的重要生理特性，并表明它们可能构成编码感觉信息的基础。
