突触通讯中的分子噪声
Molecular Noise In Synaptic Communication
摘要：
在突触分子通讯（MC）中，神经递质（NT）对突触后受体的激活受随机反应扩散过程的控制。突触MC的这种随机性有助于突触后细胞中电化学下游信号的随机性，称为突触后膜电位（PSP）。由于PSP的随机性与神经计算和学习有关，因此表征PSP的统计数据至关重要。然而，突触反应-扩散过程的统计表征是困难的，因为NT与受体的可逆双分子反应使得系统非线性。因此，目前还没有可用的模型来表征突触后受体激活统计数据对PSP的影响。在这项工作中，我们提出了一个新的统计模型的突触反应扩散过程的化学主方程（CME）。我们进一步提出了一种新的数值方法，可以有效地计算CME，我们使用这种方法来表征PSP的统计。最后，我们提出了随机粒子为基础的计算机模拟，验证所提出的模型的结果。我们发现，突触传递的生物物理参数形状的受体激活的自协方差，并最终统计的PSP。我们的研究结果表明，突触后细胞对突触信号的处理有效地减轻了突触噪声，同时保留了突触信号的统计特性。本文的结果有助于更好地理解突触信号传递的随机性对神经元信息处理的影响。
英文摘要：
In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.
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