Burnod Y, Korn H
Laboratoire de Neurobiologie Cellulaire, Institut National de la Santé et de la Recherche Médicale, Paris, France.
Proc Natl Acad Sci U S A. 1989 Jan;86(1):352-6. doi: 10.1073/pnas.86.1.352.
Neuronal membrane potentials vary continuously due largely to background synaptic noise produced by ongoing discharges in their presynaptic afferents and shaped by probabilistic factors of transmitter release. We investigated how the random activity of an identified population of interneurons with known release properties influences the performance of central cells. In stochastic models such as thermodynamic ones, the probabilistic input-output function of a formal neuron is sigmoid, having its maximal slope inversely related to a variable called "temperature." Our results indicate that, for a biological neuron, the probability that given excitatory input signals reach threshold is also sigmoid, allowing definition of a temperature that is proportional to the mean number of quanta comprising noise and can be modified by activity in the presynaptic network, a notion which could be included in neural models. By introducing uncertainty to the input-output relation of central neurons, synaptic noise could be a critical determinant of neuronal computational systems, allowing assemblies of cells to undergo continuous transitions between states.
神经元膜电位持续变化，这主要归因于其突触前传入神经持续放电产生的背景突触噪声，并由递质释放的概率因素所塑造。我们研究了一群具有已知释放特性的特定中间神经元的随机活动如何影响中枢细胞的性能。在诸如热力学模型这样的随机模型中，形式神经元的概率输入 - 输出函数呈S形，其最大斜率与一个称为“温度”的变量成反比。我们的结果表明，对于生物神经元，给定的兴奋性输入信号达到阈值的概率也是S形的，这使得能够定义一个与构成噪声的量子平均数成正比且可由突触前网络活动改变的温度，这一概念可纳入神经模型。通过在中枢神经元的输入 - 输出关系中引入不确定性，突触噪声可能是神经元计算系统的关键决定因素，使细胞集合能够在不同状态之间进行连续转换。
