UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France.
Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I, Forschungszentrum Jülich, Jülich, Germany.
Front Neural Circuits. 2018 Jul 12;12:52. doi: 10.3389/fncir.2018.00052. eCollection 2018.
Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation () and execution (). We find that the FF significantly decreases from to , while the CV2 increases. The more regular firing (expressed by a low CV2) during is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV. This expectation was met during , but not during where FF ≫ CV2. Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.
大脑中到处都存在着爆发活动的可变性，但对其上下文相关性却知之甚少。通过尖峰计数变异性的福纳系数（FF）和尖峰时间不规则性的变异系数（CV）分别估计，可反映长、短时间尺度上的变异性。我们在猴子运动皮层中比较了两种行为情境（运动准备（）和执行（）），共同分析了 FF 和局部变异系数（CV2）。我们发现，FF 从 显著降低到 ，而 CV2 增加。在 期间更规则的放电（由低 CV2 表示）与局部场电位（LFP）β振荡的功率增加以及这些振荡的尖峰锁相有关。在更新过程中，这是用于在静态输入条件下爆发活动的广泛使用的模型，这两个度量都相关，FF ≈ CV。在 期间，这一期望得到了满足，但在 期间，FF ≫ CV2 却没有得到满足。我们的解释是，在运动准备期间，持续的大脑过程导致网络状态发生变化，从而导致试验间的高度变异性（由高 FF 表示）。在运动执行期间，网络被招募来执行刻板的运动任务，从而产生可靠的单个神经元输出。我们的解释是基于最近的计算模型，这些模型产生了非静态的网络条件。
