CORTICAL ACTIVITY FLIPS AMONG QUASI-STATIONARY STATES
CORTICAL ACTIVITY FLIPS AMONG QUASI-STATIONARY STATES
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DOI：
10.1073/pnas.92.19.8616
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发表时间：
1995-09-12
影响因子：
11.1
通讯作者：
VAADIA, E
中科院分区：
文献类型：
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作者：
ABELES, M;BERGMAN, H;VAADIA, E
将行为猴多个单个皮质神经元锋电位序列的并行记录作为隐马尔可夫过程进行分析，将并行锋电位序列视为多变量Poisson过程，其向量放电率随时间变化。作为这种方法的结果，完整的记录可以被分割成一系列的几个统计上区分的隐藏状态，其动态被建模为一阶马尔可夫链。这种方法的生物学有效性和好处被检查在几个独立的方式：（i）统计一致性的分割和其对应的动物的行为;（ii）通过模型获得的活动的集体翻转的直接测量;以及（iii）记录的锋电位序列之间的分段和成对短期互相关之间的关系。还对上述每一项检查进行了与替代数据的比较，以确保它们的显著性，我们的研究结果表明，存在着分离良好的活动状态，在这些状态中，放电率近似稳定。根据我们目前的数据，我们可以可靠地区分六到八个这样的状态，状态之间的转换是快速的，并且与几个神经元的放电率的伴随变化有关，不同的神经活动状态一致地反映了不同的行为模式和刺激，而且，神经元之间的成对相关性在不同状态之间变化很大，支持这些不同的状态是由许多神经元的合作行动所带来的假设。
Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process, The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, whose dynamics are modeled as a first-order Markov chain, The biological validity and benefits of this approach were examined in several independent ways: (i) the statistical consistency of the segmentation and its correspondence to the behavior of the animal; (ii) direct measurement of the collective flips of activity, obtained by the model; and (iii) the relation between the segmentation and the pair-wise shortterm cross correlations between the recorded spike trains, Comparison with surrogate data was also carried out for each of the above examinations to assure their significance, Our results indicated the existence of well-separated states of activity, within which the firing rates were approximately stationary, With our present data we could reliably discriminate six to eight such states, The transitions between states were fast and were associated with concomitant changes of firing rates of several neurons, Different behavioral modes and stimuli were consistently reflected by different states of neural activity, Moreover, the pair-wise correlations between neurons varied considerably between the different states, supporting the hypothesis that these distinct states were brought about by the cooperative action of many neurons.