Burak Yoram, Lewallen Sam, Sompolinsky Haim
Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Neural Comput. 2009 Aug;21(8):2269-308. doi: 10.1162/neco.2009.07-08-830.
We consider a threshold-crossing spiking process as a simple model for the activity within a population of neurons. Assuming that these neurons are driven by a common fluctuating input with gaussian statistics, we evaluate the cross-correlation of spike trains in pairs of model neurons with different thresholds. This correlation function tends to be asymmetric in time, indicating a preference for the neuron with the lower threshold to fire before the one with the higher threshold, even if their inputs are identical. The relationship between these results and spike statistics in other models of neural activity is explored. In particular, we compare our model with an integrate-and-fire model in which the membrane voltage resets following each spike. The qualitative properties of spike cross-correlations, emerging from the threshold-crossing model, are similar to those of bursting events in the integrate-and-fire model. This is particularly true for generalized integrate-and-fire models in which spikes tend to occur in bursts, as observed, for example, in retinal ganglion cells driven by a rapidly fluctuating visual stimulus. The threshold-crossing model thus provides a simple, analytically tractable description of event onsets in these neurons.
我们将阈值穿越尖峰过程视为神经元群体活动的一个简单模型。假设这些神经元由具有高斯统计特性的共同波动输入驱动，我们评估了具有不同阈值的成对模型神经元中尖峰序列的互相关性。这种相关函数在时间上往往是不对称的，这表明即使输入相同，阈值较低的神经元也倾向于在阈值较高的神经元之前放电。我们探讨了这些结果与其他神经活动模型中尖峰统计之间的关系。特别是，我们将我们的模型与一种积分发放模型进行了比较，在该模型中，每次尖峰后膜电压都会重置。从阈值穿越模型中出现的尖峰互相关性的定性特性与积分发放模型中爆发事件的特性相似。对于广义积分发放模型来说尤其如此，在这种模型中，尖峰往往会成簇出现，例如在由快速波动的视觉刺激驱动的视网膜神经节细胞中就观察到了这种情况。因此，阈值穿越模型为这些神经元中的事件起始提供了一个简单的、易于分析处理的描述。
