Gutkin B S, Ermentrout G B
Program in Neurobiology, University of Pittsburgh, PA 15260, USA.
Neural Comput. 1998 Jul 1;10(5):1047-65. doi: 10.1162/089976698300017331.
We propose a biophysical mechanism for the high interspike interval variability observed in cortical spike trains. The key lies in the nonlinear dynamics of cortical spike generation, which are consistent with type I membranes where saddle-node dynamics underlie excitability (Rinzel & Ermentrout, 1989). We present a canonical model for type I membranes, the theta-neuron. The theta-neuron is a phase model whose dynamics reflect salient features of type I membranes. This model generates spike trains with coefficient of variation (CV) above 0.6 when brought to firing by noisy inputs. This happens because the timing of spikes for a type I excitable cell is exquisitely sensitive to the amplitude of the suprathreshold stimulus pulses. A noisy input current, giving random amplitude "kicks" to the cell, evokes highly irregular firing across a wide range of firing rates; an intrinsically oscillating cell gives regular spike trains. We corroborate the results with simulations of the Morris-Lecar (M-L) neural model with random synaptic inputs: type I M-L yields high CVs. When this model is modified to have type II dynamics (periodicity arises via a Hopf bifurcation), however, it gives regular spike trains (CV below 0.3). Our results suggest that the high CV values such as those observed in cortical spike trains are an intrinsic characteristic of type I membranes driven to firing by "random" inputs. In contrast, neural oscillators or neurons exhibiting type II excitability should produce regular spike trains.
我们提出了一种生物物理机制，用于解释在皮层尖峰序列中观察到的高峰间间隔变异性。关键在于皮层尖峰产生的非线性动力学，这与I型膜一致，其中鞍结动力学是兴奋性的基础（林泽尔和埃门特劳特，1989年）。我们提出了一种I型膜的典型模型，即θ神经元。θ神经元是一种相位模型，其动力学反映了I型膜的显著特征。当由噪声输入引发放电时，该模型产生变异系数（CV）高于0.6的尖峰序列。之所以会这样，是因为I型可兴奋细胞的尖峰时间对阈上刺激脉冲的幅度极为敏感。一个噪声输入电流，给细胞随机的幅度“冲击”，会在很宽的放电率范围内引发高度不规则的放电；而一个内在振荡的细胞则会产生规则的尖峰序列。我们通过对具有随机突触输入的莫里斯 - 莱卡（M - L）神经模型的模拟来证实这些结果：I型M - L产生高CV值。然而，当将该模型修改为具有II型动力学（通过霍普夫分岔产生周期性）时，它会产生规则的尖峰序列（CV低于0.3）。我们的结果表明，在皮层尖峰序列中观察到的高CV值是由“随机”输入驱动放电的I型膜的固有特征。相比之下，神经振荡器或表现出II型兴奋性的神经元应该产生规则的尖峰序列。
