Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials.
Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials.
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DOI：
10.3389/fncom.2016.00110
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发表时间：
2016
影响因子：
3.2
通讯作者：
Toyoizumi T
中科院分区：
文献类型：
--
作者：
Lankarany M;Heiss JE;Lampl I;Toyoizumi T
先进的统计方法已经能够对膜电位记录的潜在兴奋性和抑制性突触传导（SCs）进行逐次推断。兴奋性和抑制性SCs的同时推断揭示了神经活动背后的神经回路，并促进了我们对神经信息处理的理解。传统的贝叶斯方法可以根据一次观察到的膜电位推断出兴奋性和抑制性SCs。然而，如果有多个记录的试验可用，这通常会导致次优估计，因为它们忽略了跨试验的共同统计（突触输入（si））。在这里，我们建立了一个新的期望最大化（EM）算法，该算法通过利用多个记录的试验来提取跨试验的共同SI统计量，从而改进了单试验贝叶斯方法。在本文中，所提出的EM算法被嵌入到多个记录试验的并行卡尔曼滤波器或粒子滤波器中，以整合它们的输出以迭代更新公共SI统计。然后使用这些统计数据来推断单个试验的兴奋性和抑制性SCs。我们证明了多次试验卡尔曼滤波（MtKF）和粒子滤波（MtPF）相对于相应的单次试验方法的优越性能。虽然已知兴奋性和抑制性SCs的相对估计误差取决于注入细胞的电流水平，但我们使用MtKF的数值模拟表明，使用最佳电流水平可以可靠地推断出兴奋性和抑制性SCs。最后，我们通过模拟研究验证了我们技术的鲁棒性和适用性，并将MtKF应用于大鼠桶皮质的体内数据。
Advanced statistical methods have enabled trial-by-trial inference of the underlying excitatory and inhibitory synaptic conductances (SCs) of membrane-potential recordings. Simultaneous inference of both excitatory and inhibitory SCs sheds light on the neural circuits underlying the neural activity and advances our understanding of neural information processing. Conventional Bayesian methods can infer excitatory and inhibitory SCs based on a single trial of observed membrane potential. However, if multiple recorded trials are available, this typically leads to suboptimal estimation because they neglect common statistics (of synaptic inputs (SIs)) across trials. Here, we establish a new expectation maximization (EM) algorithm that improves such single-trial Bayesian methods by exploiting multiple recorded trials to extract common SI statistics across the trials. In this paper, the proposed EM algorithm is embedded in parallel Kalman filters or particle filters for multiple recorded trials to integrate their outputs to iteratively update the common SI statistics. These statistics are then used to infer the excitatory and inhibitory SCs of individual trials. We demonstrate the superior performance of multiple-trial Kalman filtering (MtKF) and particle filtering (MtPF) relative to that of the corresponding single-trial methods. While relative estimation error of excitatory and inhibitory SCs is known to depend on the level of current injection into a cell, our numerical simulations using MtKF show that both excitatory and inhibitory SCs are reliably inferred using an optimal level of current injection. Finally, we validate the robustness and applicability of our technique through simulation studies, and we apply MtKF to in vivo data recorded from the rat barrel cortex.
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影响因子：
64.8
作者：
通讯作者：
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影响因子：
5.3
作者：
Heiss, Jaime E.;Katz, Yonatan;Lampl, Ilan
通讯作者：
Lampl, Ilan
影响因子：
64.8
作者：
Wehr, M;Zador, AM
通讯作者：
Zador, AM
影响因子：
5.3
作者：
Katz, Yonatan;Heiss, Jaime E.;Lampl, Ilan
通讯作者：
Lampl, Ilan
影响因子：
1.2
作者：
Vich, Catalina;Guillamon, Antoni
通讯作者：
Guillamon, Antoni