Schneidman E, Freedman B, Segev I
Department of Neurobiology, Hebrew University, Jerusalem, Israel.
Neural Comput. 1998 Oct 1;10(7):1679-703. doi: 10.1162/089976698300017089.
The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to DC and fluctuating current input in neocortical neurons, as reported by Mainen & Sejnowski (1995). For DC inputs, spike timing is highly unreliable; the reliability and precision are significantly increased for fluctuating current input. This behavior is critically determined by the relatively small number of excitable channels that are opened near threshold for spike firing rather than by the total number of channels that exist in the membrane patch. Channel fluctuations, together with the inherent bistability in the HH equations, give rise to three additional experimentally observed phenomena: subthreshold oscillations in the membrane voltage for DC input, "spontaneous" spikes for subthreshold inputs, and "missing" spikes for suprathreshold inputs. We suggest that the noise inherent in the operation of ion channels enables neurons to act as "smart" encoders. Slowly varying, uncorrelated inputs are coded with low reliability and accuracy and, hence, the information about such inputs is encoded almost exclusively by the spike rate. On the other hand, correlated presynaptic activity produces sharp fluctuations in the input to the postsynaptic cell, which are then encoded with high reliability and accuracy. In this case, information about the input exists in the exact timing of the spikes. We conclude that channel stochasticity should be considered in realistic models of neurons.
利用随机霍奇金-赫胥黎（HH）模型研究了由实际数量众多的离子通道组成的等电位膜片的发放可靠性和精度。与确定性HH模型形成鲜明对比的是，受生物物理学启发的随机模型定性地再现了新皮层神经元中响应直流和波动电流输入时发放动作电位的不同可靠性和精度特征，如梅宁和塞乔夫斯基（1995年）所报道的那样。对于直流输入，动作电位的发放时间极不可靠；对于波动电流输入，可靠性和精度显著提高。这种行为关键取决于在动作电位发放阈值附近打开的相对较少数量的可兴奋通道，而不是膜片中存在的通道总数。通道波动与HH方程中固有的双稳性一起，导致了另外三个实验观察到的现象：直流输入时膜电压的阈下振荡、阈下输入时的“自发”动作电位以及阈上输入时的“缺失”动作电位。我们认为离子通道运作中固有的噪声使神经元能够充当“智能”编码器。缓慢变化、不相关的输入以低可靠性和准确性进行编码，因此，关于此类输入的信息几乎完全由动作电位发放率编码。另一方面，相关的突触前活动会使突触后细胞的输入产生急剧波动，然后以高可靠性和准确性进行编码。在这种情况下，关于输入的信息存在于动作电位的精确发放时间中。我们得出结论，在神经元的现实模型中应考虑通道的随机性。
