Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

中文翻译：

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神经电路的峰值定时精度。

峰值时间被认为是由神经元和神经元回路执行的感官信息编码和计算中的关键因素。但是，由神经元和突触中固有的随机机制引起的大量噪声和可变性会降低尖峰定时精度。计算建模可以帮助解密神经元电路利用的机制，以调节时序精度。在本文中，我们利用半分析技术（从先前开发的电子电路方法改编而成）对神经元电路进行随机表征。这些技术的速度比传统的蒙特卡洛（Monte Carlo）类型仿真快几个数量级，可用于直接计算尖峰定时抖动方差，功率谱密度，相关函数，以及神经元回路操作的其他随机特征。我们考虑了三个不同的神经元电路图案：反馈抑制，突触整合和突触耦合。首先，我们表明通过反馈抑制，尖峰定时精度和尖峰神经元的能量效率均得到改善。我们将揭示实现这一目标的基本机制。然后，我们证明了神经元可以通过突触积分来改善来自多个来源的突触输入的时序精度：积分神经元的输出尖峰相位与该阶段的样本平均值具有相同的方差其输入。最后，我们发现，在完全连接的网络中，神经元之间的弱突触耦合使它们能够像具有较大膜面积的单个神经元一样运作，