论文标题
快速神经振荡的确切低维描述，发射率低
Exact low-dimensional description for fast neural oscillations with low firing rates
论文作者
论文摘要
最近，针对确定性，二次集成和开火（QIF）神经元的大型网络，神经元活性的低维模型已被精确得出。这种发射速率模型（FRM）描述了通过将神经元子集的频率锁定到全局振荡频率的频率锁定的出现。然而，这种模型描述现实神经元状态的适合性受到事实的严重挑战：在快速集体振荡的发作中，与全球振荡频率相比，神经元的放电通常非常不规则，发射速率较低。在这里，我们将理论扩展到QIF神经元的精确FRM，以包括噪声，并表明在快速振荡期间，在低射击速率下显示出不规则放电的网络，由与确定性网络完全相同的进化方程来控制。我们的结果调和了传统上对神经元同步的两种观点，并升级了精确的FRM来描述广泛的生物学现实神经元状态。
Recently, low-dimensional models of neuronal activity have been exactly derived for large networks of deterministic, Quadratic Integrate-and-Fire (QIF) neurons. Such firing rate models (FRM) describe the emergence of fast collective oscillations (>30~Hz) via the frequency-locking of a subset of neurons to the global oscillation frequency. However, the suitability of such models to describe realistic neuronal states is seriously challenged by fact that during episodes of fast collective oscillations, neuronal discharges are often very irregular and have low firing rates compared to the global oscillation frequency. Here we extend the theory to derive exact FRM for QIF neurons to include noise, and show that networks of stochastic neurons displaying irregular discharges at low firing rates during episodes of fast oscillations, are governed by exactly the same evolution equations as deterministic networks. Our results reconcile two traditionally confronted views on neuronal synchronization, and upgrade the applicability of exact FRM to describe a broad range of biologically realistic neuronal states.
