A simple model of long-term spike train regularization
A simple model of long-term spike train regularization
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DOI：
10.1162/08997660260028629
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发表时间：
2002-07-01
影响因子：
2.9
通讯作者：
Nelson, ME
中科院分区：
文献类型：
--
作者：
Brandman, R;Nelson, ME
描述了一种简单的棘波产生模型，该模型在棘波间期(ISI)序列中产生负相关性，并导致长期的棘波序列规则化。这种正则化可以通过检查大k(尖峰i和尖峰i+k之间的时间)的k阶区间分布的方差来看到。如果连续的ISIS是不相关的，那么这种差异将比预期的要小得多。这种规律化效应已经在电感觉传入神经纤维的棘波序列中观察到，并且可以导致在棘波序列数据中编码的微弱信号的可检测性的显著改善(Ratnam&Nelson，2000)。在这里，我们提出了一个简单的神经模型，在该模型中，负的ISI相关性和长期的棘波序列正则化产生于与动态棘波阈值相关的不应性效应。我们的模型来源于最近由其他研究者(Chacron，Longtin，St.-Hilaire，Maler，2000；Chacron，Longtin，&Maler，2001)开发的更详细的电感觉传入动力学模型。这个模型的核心是一个动态尖峰阈值，它在尖峰之后瞬间升高，随后衰减，直到产生下一个尖峰。在这里，我们提出了一个简化的版本-线性自适应阈值模型-它包含一个状态变量和三个自由参数，它们控制自发ISI分布的平均值和变异系数以及驱动响应的频率特性。我们表明，与动态阈值相关的耐受效应导致了长时间尺度上的棘波序列的正则化。此外，我们还证明了这种正则化增强了由线性自适应阈值模型编码的弱信号的可检测性。尽管受到电感觉传入神经纤维特性的启发，这种规律化效应可能在其他神经系统中发挥重要作用，在这些神经系统中，必须在嘈杂的棘波序列中可靠地检测到微弱信号。当对呈现这种ISI相关结构的神经元系统进行建模时，线性自适应阈值模型可能比缺乏长期规则化效应的传统更新过程模型提供更合适的起点。
A simple model of spike generation is described that gives rise to negative correlations in the interspike interval (ISI) sequence and leads to long-term spike train regularization. This regularization can be seen by examining the variance of the kth-order interval distribution for large k (the times between spike i and spike i+k). The variance is much smaller than would be expected if successive ISIs were uncorrelated. Such regularizing effects have been observed in the spike trains of electrosensory afferent nerve fibers and can lead to dramatic improvement in the detectability of weak signals encoded in the spike train data (Ratnam & Nelson, 2000). Here, we present a simple neural model in which negative ISI correlations and long-term spike train regularization arise from refractory effects associated with a dynamic spike threshold. Our model is derived from a more detailed model of electrosensory afferent dynamics developed recently by other investigators (Chacron, Longtin, St.-Hilaire, Maler, 2000; Chacron, Longtin, & Maler, 2001). The core of this model is a dynamic spike threshold that is transiently elevated following a spike and subsequently decays until the next spike is generated. Here, we present a simplified version-the linear adaptive threshold model-that contains a single state variable and three free parameters that control the mean and coefficient of variation of the spontaneous ISI distribution and the frequency characteristics of the driven response. We show that refractory effects associated with the dynamic threshold lead to regularization of the spike train on long timescales. Furthermore, we show that this regularization enhances the detectability of weak signals encoded by the linear adaptive threshold model. Although inspired by properties of electrosensory afferent nerve fibers, such regularizing effects may play an important role in other neural systems where weak signals must be reliably detected in noisy spike trains. When modeling a neuronal system that exhibits this type of ISI correlation structure, the linear adaptive threshold model may provide a more appropriate starting point than conventional renewal process models that lack long-term regularizing effects.