A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.
A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.
复制标题
一个动态的贝叶斯模型，用于表征决策过程中跨神经元相互作用。
DOI：
10.1080/01621459.2015.1116988
复制
发表时间：
2016
影响因子：
3.7
通讯作者：
Shahbaba B
中科院分区：
文献类型：
--
作者：
Zhou B;Moorman DE;Behseta S;Ombao H;Shahbaba B
本文的目标是开发一种新的统计模型，用于研究决策过程中的跨神经元锋电位序列相互作用。一个人要成功地完成决策任务，必须发生一些时间上有组织的事件：必须检测到刺激，必须评估潜在的结果，必须执行或抑制行为，必须体验结果（如奖励或无奖励）。由于这一过程的复杂性，决策很可能是由大量神经元之间的时间精确相互作用编码的。然而，大多数现有的统计模型不足以分析这种现象，因为它们只提供了一个随时间变化的相互作用的综合衡量标准。为了解决这个相当大的限制，我们提出了一个动态贝叶斯模型，捕捉神经元活动的时变性质（如神经元之间的相互作用的时变强度）。所提出的方法产生的结果，揭示了新的见解，在决策过程中的前额叶皮层的人口编码的动态性质。在我们的分析中，我们注意到，虽然前额叶皮层中的一些神经元在奖励出现之前不会同步它们的放电活动，但另一组神经元在刺激开始后不久就会同步它们的活动。这些差异同步的神经元亚群表明了奖励寻求任务的连续性群体表示。其次，我们的分析还表明，奖励和非奖励条件下的同步程度不同。此外，该模型是可扩展的，以处理许多错误记录的神经元上的数据，并适用于分析其他类型的多变量时间序列数据的潜在结构。我们论文的补充材料（包括计算机代码）可在网上查阅。
The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.
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DOI：
10.1523/jneurosci.2753-12.2013
发表时间：
2013-02-27
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Barak O;Rigotti M;Fusi S
通讯作者：
Fusi S
影响因子：
2.7
作者：
Bien, Jacob;Tibshirani, Robert J.
通讯作者：
Tibshirani, Robert J.
影响因子：
3.7
作者：
FAHRMEIR, L
通讯作者：
FAHRMEIR, L
DOI：
10.1111/j.1467-9868.2008.00663.x
发表时间：
2008-09-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Banerjee S;Gelfand AE;Finley AO;Sang H
通讯作者：
Sang H
DOI：
10.1111/1467-9876.00229
发表时间：
2001-01-01
影响因子：
1.6
作者：
Fahrmeir, L;Lang, S
通讯作者：
Lang, S