AI 中文总结
研究接收独立兴奋性和抑制性突触前动作电位的突触后神经元的ISI统计，通过构建积分发放神经元模型，分析固定和自适应阈值电位下的ISI噪声，揭示其与输入频率关系，还研究超极化自适应阈值电位，为理解神经元间信息处理提供系统随机分析。
AI 中文摘要
神经元通信部分由神经元放电率的变化介导。连续神经元放电之间的时间间隔称为尖峰间隔（ISI），量化其统计信息对理解神经元通信很重要。本文研究了接收独立兴奋性和抑制性突触前动作电位的突触后神经元的ISI统计（EI电路）。该电路被建模为经典的积分发放神经元，并针对固定和自适应阈值电位研究ISI统计。首先研究了去极化自适应阈值模型，分析表明在相同平均ISI下，该模型中作为变异系数量化的ISI噪声比固定阈值模型大。模拟显示ISI噪声根据兴奋性和抑制性输入频率可为次指数或超指数。接着研究了超极化自适应阈值电位，此模型表明突触后神经元仅由抑制性输入驱动时也能产生动作电位。此外，还表征了兴奋性和抑制性输入的模型参数的均值和噪声特征。总之，这项工作为动作电位生成的自适应阈值模型提供了系统的随机分析，以了解它们在神经元间信息处理中的作用。
英文摘要
Neuronal communication is mediated in part by changes in neuronal firing rates. The time interval between successive neuronal firings is referred to as the inter-spike interval (ISI), and quantifying its statistics is important for understanding neuronal communication. This paper studies the ISI statistics of a postsynaptic neuron receiving independent excitatory and inhibitory presynaptic action potentials (EI circuit). This circuit is modeled as a classical integrate-and-fire neuron, and the ISI statistics are investigated for both fixed and adaptive threshold potentials. First, a depolarizing adaptive threshold model is studied, where the threshold potential increases with the postsynaptic membrane potential. Our analysis shows that the ISI noise, quantified as the coefficient of variation, is larger in the adaptive threshold model compared to the fixed threshold model for the same mean ISI. Additionally, simulations reveal that the ISI noise can be either hypo- or hyper-exponential (defined as ISI noise smaller or larger than one, respectively) depending on the frequencies of excitatory and inhibitory inputs. Next, a hyperpolarizing adaptive threshold potential is studied, where the threshold decreases as the membrane potential hyperpolarizes. Interestingly, this model shows that the postsynaptic neuron can generate action potentials (APs) when driven solely by inhibitory inputs. Furthermore, mean and noise signatures are characterized across model parameters for both excitatory and inhibitory inputs. In summary, this work provides a systematic stochastic analysis of adaptive threshold models for AP generation to understand their role in interneuronal information processing.
