传递熵能否推断认知处理神经回路中的信息流？
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
AI总结：
本研究针对运动检测与声音定位任务的人工神经回路，测试传递熵推断信息流的准确性，发现其存在因果误判问题且程度依任务而异，强调需理解认知处理中信息流的基础逻辑。
摘要：
要推断任意智能体网络中的信息流，首要的是建立节点间的因果时间关系。实用且自动化的因果推断方法难以寻觅，这也是当前持续研究的课题。香农信息仅能检测相关性，而目前已有若干“有向信息”的信息论概念成功在部分系统中检测出因果关系，这在神经科学领域尤为突出。但近期研究表明，部分有向信息度量有时无法充分估计因果关系的程度，甚至无法识别系统组件间已存在的因果效应关系，尤其是当神经元以加密方式作用于效应神经元时。\n本文针对两项基础认知任务——运动检测与声音定位，测试了在生成人工神经回路的演化过程中，加密逻辑出现的频率。我们还将基于传递熵概念、从运行中的数字大脑记录的活动时间序列推断出的信息流，与基于连接性和回路逻辑构建的因果影响基准模型进行对比，测试该推断的准确性。\n结果表明，传递熵有时会在因果关系存在时无法推断出来，有时又会在不存在因果关系时误判存在因果关联。不过，错误推断的程度很大程度上取决于所研究的认知任务。这些结果凸显了理解认知处理中促成信息流的基础逻辑过程、并量化其在任意给定神经系统中相关性的重要性。
英文摘要：
To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes. Practical and automated methods that can infer causality are difficult to find, and the subject of ongoing research. While Shannon information only detects correlation, there are several information-theoretic notions of "directed information" that have successfully detected causality in some systems, in particular in the neuroscience community. However, recent work has shown that some directed information measures can sometimes inadequately estimate the extent of causal relations, or even fail to identify existing cause-effect relations between components of systems, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks: motion detection and sound localization. We also test whether activity time-series recorded from behaving digital brains can infer information flow using the transfer entropy concept, when compared to a ground-truth model of causal influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer causality when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
