分解信息流动的比特归属，揭示因果关系中过去与未来的具体贡献。
Which bits went where? Past and future transfer entropy decomposition with the information bottleneck
- 用信息瓶颈压缩时间序列，定位信息流中的比特来源与去向。
- 在合成系统和小鼠行为神经数据中成功识别出过去与未来的信息贡献。
- 适合研究复杂系统中动态因果机制的学者，尤其关注时间过程交互者。
无论研究对象是鱼群、神经元集合还是大气与海洋的相互作用过程，传递熵都能衡量时间序列间的信息流动并检测潜在因果关系。与互信息类似，传递熵通常以单一数值概括共享变异量，但更精细的分析可能揭示更多过程细节。本文提出一种方法，将传递熵分解并定位信息流动中源过程的过去与接收过程的未来所贡献的比特。我们采用信息瓶颈（IB）对时间序列进行压缩，并识别出转移的熵。该方法应用于多个合成递归过程及一项小鼠的实时行为与神经活动实验数据集。结果揭示了信息流动内部的细微动态，为复杂系统中时序过程相互作用的深入探索奠定了基础。
原文摘要 · Abstract (English)
Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the originating process's past and that of the receiving process's future. We employ the information bottleneck (IB) to compress the time series and identify the transferred entropy. We apply our method to decompose the transfer entropy in several synthetic recurrent processes and an experimental mouse dataset of concurrent behavioral and neural activity. Our approach highlights the nuanced dynamics within information flow, laying a foundation for future explorations into the intricate interplay of temporal processes in complex systems.
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