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Modifying spiking precision in conductance-based neuronal models

机译:在基于电导的神经元模型中修改加标精度

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The temporal precision of a neuron's spiking can be characterized by calculating its "jitter," defined as the standard deviation of the timing of individual spikes in response to repeated presentations of a stimulus. Sub-millisecond jitters have been measured for neurons in a variety of experimental systems and appear to be functionally important in some instances. We have investigated how modifying a neuron's maximal conductances affects jitter using the leaky integrate-and-fire (LIF) model and an eight-conductance Hodgkin-Huxley type (HH8) model. We observed that jitter can be largely understood in the LJF model in terms of the neuron's filtering properties. In the HH8 model we found the role of individual conductances in determining jitter to be complicated and dependent on the model's spiking properties. Distinct behaviors were observed for populations with slow (<11.5Hz) and fast (>11.5Hz) spike rates and appear to be related to differences in a particular channel's activity at times just before spiking occurs.
机译:神经元尖峰的时间精度可以通过计算其“抖动”来表征,“抖动”定义为响应刺激重复出现的单个峰值时间的标准偏差。在各种实验系统中,已经为神经元测量了亚毫秒级抖动,并且在某些情况下在功能上似乎很重要。我们已经研究了如何使用泄漏集成发射(LIF)模型和八电导霍奇金-赫克斯利(HH8)模型修改神经元的最大电导率对抖动的影响。我们观察到,就神经元的过滤特性而言,可以在LJF模型中很大程度上理解抖动。在HH8模型中,我们发现单个电导在确定抖动方面的作用很复杂,并且取决于模型的尖峰特性。在峰值频率较低(<11.5Hz)和峰值频率(> 11.5Hz)的人群中观察到了不同的行为,这些行为与峰值发生之前的特定通道活动的差异有关。

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