Mixed vine copula flows for flexible modeling of neural dependencies.
Mixed vine copula flows for flexible modeling of neural dependencies.
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DOI：
10.3389/fnins.2022.910122
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发表时间：
2022
影响因子：
4.3
通讯作者：
Onken, Arno
中科院分区：
文献类型：
--
作者：
Mitskopoulos, Lazaros;Amvrosiadis, Theoklitos;Onken, Arno
关键词：
复杂神经群体反应的记录为增进我们对多个尺度的神经信息处理的理解和提高脑机接口的性能提供了独特的机会。然而，大多数现有的分析技术都无法捕捉到协调一致的群体活动中相互作用的复杂性。基于 Vine copula 的方法已被证明可以成功地解决群体内复杂的高阶依赖关系，摆脱单神经元统计数据的束缚。然而，大多数应用程序都集中在参数联结函数上，这些联结函数存在错误指定依赖结构的风险。为了避免这种风险，我们通过使用神经样条流（NSF）对单神经元边缘和联结采用完全非参数方法。我们在具有各种形式的依赖结构和不同维度的连续和离散类型的模拟数据上验证了 NSF 框架。总体而言，NSF 的表现与现有的非参数估计器类似，同时允许更快、更灵活的采样，这也使得能够更快地对 copula 熵进行蒙特卡罗估计。此外，我们的框架能够捕获在动物在虚拟现实环境中进行视觉学习任务期间记录在小鼠初级视觉皮层中的神经元反应中的低阶和高阶重尾依赖性。这些发现强调了协调神经元活动的复杂性中经常被忽视的一个方面，这对于理解和破译神经技术应用的集体神经动力学非常重要。
Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications.
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