PDF(Pubmed)
Abstract：
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
摘要：
新皮层神经元的尖峰活动表现出惊人的变异性，即使这些网络是由相同的刺激驱动的。神经元的近似泊松放电导致了这些神经网络在异步状态下运行的假设。在异步状态下，神经元彼此独立地放电，因此，神经元经历同步突触输入的概率极低。虽然异步神经元的模型导致观察到的尖峰变异性，尚不清楚异步状态是否也可以解释亚阈值膜电位变异性的水平。我们提出了一个新的分析框架，以严格量化单个基于电导的神经元的亚阈值变异性，以响应具有规定的同步程度的突触输入。从技术上讲,我们利用可交换性理论通过基于跳跃过程的突触驱动对输入同步性进行建模；然后，我们对神经元模型的平稳响应进行矩分析，该模型具有全导或全导，忽略了尖峰后复位。因此,我们生产精确的，膜电压的前两个静止时刻的可解释闭合形式，明确依赖于输入突触数，优势,和同步。对于生物物理相关参数，我们发现，只有在有限数量的大突触驱动下，异步机制才会产生现实的亚阈值变异性（电压方差=4-9mV2），与强大的丘脑驱动兼容。相比之下,我们发现，通过密集的皮质-皮质输入实现现实的亚阈值变异性需要包括弱但非零的输入同步性，与测量的成对尖峰相关性一致。我们还表明，没有同步，在突触权重消失的情况下，所有缩放限制的神经变异性平均为零，独立于任何平衡状态假设。该结果挑战了异步状态的平均场理论的理论基础。
