What does scalar timing tell us about neural dynamics?
What does scalar timing tell us about neural dynamics?
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DOI：
10.3389/fnhum.2014.00438
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发表时间：
2014
影响因子：
2.9
通讯作者：
Gavornik JP
中科院分区：
文献类型：
--
作者：
Shouval HZ;Hussain Shuler MG;Agarwal A;Gavornik JP
作为著名的韦伯定律的时域推广的“标量定时定律”指出，估计时间间隔的误差与间隔的持续时间线性地成比例。线性缩放在人类和动物模型中得到了广泛的研究，并保持了几个数量级，尽管迄今为止还没有对其生理基础的一致解释。从行为变异性源于神经变异性的假设出发，这项工作展示了如何推导出与标量定时一致的放电率函数。我们发现，发射率函数的对数功率的形式，和一组参数，取决于尖峰计数统计，可以占标量定时。我们的推导依赖于线性近似，但我们使用模拟来验证理论，并表明对数功率发射率函数在很大的时间和参数范围内导致标量定时。模拟结果与我们模型的预测相匹配，尽管我们的初始公式导致了对高估的轻微偏差，可以使用简单的迭代方法来学习决策阈值来纠正。
The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold.
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