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皮质尖峰序列在大脑状态转变过程中的信息含量。

Information content in cortical spike trains during brain state transitions.

机构信息

Instituto de Neurociencias de Alicante, Universidad Miguel Hernández-CSIC, San Juan de Alicante, Spain.

出版信息

J Sleep Res. 2013 Feb;22(1):13-21. doi: 10.1111/j.1365-2869.2012.01031.x. Epub 2012 Jun 28.

Abstract

Even in the absence of external stimuli there is ongoing activity in the cerebral cortex as a result of recurrent connectivity. This paper attempts to characterize one aspect of this ongoing activity by examining how the information content carried by specific neurons varies as a function of brain state. We recorded from rats chronically implanted with tetrodes in the primary visual cortex during awake and sleep periods. Electro-encephalogram and spike trains were recorded during 30-min periods, and 2-4 neuronal spikes were isolated per tetrode off-line. All the activity included in the analysis was spontaneous, being recorded from the visual cortex in the absence of visual stimuli. The brain state was determined through a combination of behavior evaluation, electroencephalogram and electromyogram analysis. Information in the spike trains was determined by using Lempel-Ziv Complexity. Complexity was used to estimate the entropy of neural discharges and thus the information content (Amigóet al. Neural Comput., 2004, 16: 717-736). The information content in spike trains (range 4-70 bits s(-1) ) was evaluated during different brain states and particularly during the transition periods. Transitions toward states of deeper sleep coincided with a decrease of information, while transitions to the awake state resulted in an increase in information. Changes in both directions were of the same magnitude, about 30%. Information in spike trains showed a high temporal correlation between neurons, reinforcing the idea of the impact of the brain state in the information content of spike trains.

摘要

即使没有外部刺激,由于反复连接,大脑皮层也会持续活动。本文试图通过检查特定神经元携带的信息内容如何随大脑状态的变化而变化来描述这种持续活动的一个方面。我们在清醒和睡眠期间从慢性植入四极管的大鼠的初级视觉皮层中记录。在 30 分钟的时间段内记录脑电图和尖峰序列,并离线从每个四极管中分离出 2-4 个神经元尖峰。所有被分析的活动都是自发的,是在没有视觉刺激的情况下从视觉皮层记录的。大脑状态是通过行为评估、脑电图和肌电图分析相结合来确定的。使用 Lempel-Ziv 复杂度来确定尖峰序列中的信息。复杂度用于估计神经放电的熵,从而估计信息内容(Amigó 等人,《神经计算》,2004 年,16:717-736)。在不同的大脑状态下,特别是在过渡期间,评估了尖峰序列中的信息内容(范围为 4-70 bits/s)。向更深睡眠状态的转变伴随着信息的减少,而向清醒状态的转变则导致信息的增加。这两个方向的变化幅度相同,约为 30%。尖峰序列中神经元之间存在高度的时间相关性,这加强了大脑状态对尖峰序列信息内容的影响的观点。

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