Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries.
Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries.
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DOI：
10.7554/elife.68192
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发表时间：
2022-03-22
期刊：
影响因子：
7.7
通讯作者：
Nemenman I
中科院分区：
文献类型：
--
作者：
Hernández DG;Sober SJ;Nemenman I
破译低级模式（大脑中的动作电位、蛋白质中的氨基酸等）如何驱动高级生物特征（感觉运动行为、酶功能）的问题代表了定量生物学的核心挑战。由于可以通过实验收集的数据集的大小缺乏通用方法，严重限制了我们对生物世界的理解。例如，在神经科学中，一些感觉和运动代码已被证明由精确定时的多尖峰模式组成。然而，此类模式代码的组合复杂性阻碍了对其综合分析方法的开发。因此，正如很难根据蛋白质的序列来预测其功能一样，我们仍然不知道如何根据神经活动准确预测生物体的行为。在这里，我们引入无监督贝叶斯伊辛近似（uBIA）来解决此类问题。我们展示了它在神经数据应用中的实用性，检测精确定时的尖峰模式，这些尖峰模式编码鸣禽声音系统中的特定运动行为。在发声控制区域神经元唱歌期间记录的数据中，我们的方法从小数据集中检测具有任意数量尖峰的码字，并解释码字出现的依赖性。检测这种综合运动控制词典可以提高我们对熟练运动控制和动物感觉运动学习神经基础的理解。为了进一步说明 uBIA 的实用性，我们用它来识别编码发声运动探索与典型歌曲制作的不同活动模式集。至关重要的是，我们的方法不仅可以用于分析神经系统，还可以用于理解其他生物和非生物数据集中的相关结构。
The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein’s function based on its sequence, we still do not understand how to accurately predict an organism’s behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, we used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.
影响因子：
3.5
作者：
Kelly CW;Sober SJ
通讯作者：
Sober SJ