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首页> 外文期刊>Journal of Computational Neuroscience >Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime
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Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime

机译：低信噪比条件下树突树上采样电压的最佳实验设计

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摘要

Due to the limitations of current voltage sens ing techniques, optimal filtering of noisy, undersampled voltage signals on dendritic trees is a key problem in computational cellular neuroscience. These limitations lead to voltage data that is incomplete (in the sense of only capturing a small portion of the full spatiotem poral signal) and often highly noisy. In this paper we use a Kalman filtering framework to develop optimal experimental design methods for voltage sampling. Our approach is to use a simple greedy algorithm with lazy evaluation to minimize the expected square error of the estimated spatiotemporal voltage signal. We take advantage of some particular features of the dendritic filtering problem to efficiently calculate the Kalman estimator's covariance. We test our framework with simulations of real dendritic branching structures and compare the quality of both time-invariant and time varying sampling schemes. While the benefit of using the experimental design methods was modest in the time-invariant case, improvements of 25-100% over more naive methods were found when the observation locations were allowed to change with time. We also present a heuristic approximation to the greedy algo rithm that is an order of magnitude faster while still providing comparable results.
机译：由于当前电压传感技术的局限性，在树状树上对嘈杂，欠采样电压信号进行最佳滤波是计算细胞神经科学中的关键问题。这些限制导致电压数据不完整（就其意义而言，其仅捕获了整个空间信号的一小部分），并且通常噪声很大。在本文中，我们使用卡尔曼滤波框架来开发用于电压采样的最佳实验设计方法。我们的方法是使用带有延迟评估的简单贪婪算法，以最小化估计的时空电压信号的预期平方误差。我们利用树突滤波问题的某些特定功能来有效地计算Kalman估计量的协方差。我们通过模拟真实的树枝状分支结构来测试我们的框架，并比较时不变和时变采样方案的质量。尽管在时不变的情况下使用实验设计方法的好处是微不足道的，但是当允许观察位置随时间变化时，与较幼稚的方法相比，可以提高25-100％。我们还提出了对贪婪算法的启发式近似，该算法的速度快了一个数量级，同时仍提供了可比的结果。

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