Centre for Neural Dynamics, University of Ottawa, Ottawa, Ontario, Canada.
PLoS One. 2010 Nov 8;5(11):e13651. doi: 10.1371/journal.pone.0013651.
Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate dynamics, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise.
复杂的相干动力学存在于各种神经系统中。一个典型的例子是大脑皮层区域中观察到的上下状态之间的电压跃迁。在这项工作中，我们通过一个具有上下跃迁的生物启发的随机模型来研究这一现象。该模型由一个简单的双稳态率动力学组成，其中突触电流由短期突触过程调制，这些过程引入了随机性和时间相关性。通过平均场方法和数值模拟对我们的模型进行的全面分析表明，在突触噪声的驱动下，复杂的高（上）和低（下）神经活动状态之间会出现复杂的跃迁，上状态的持续时间根据幂律分布。我们表明，实验观察到的上和下持续时间的大波动可以解释为具有足够大恢复时间的噪声动态突触的结果。静态突触不能解释这种行为，没有噪声的动态突触也不能解释。
