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引用次数: 0
摘要
语义信息论(SIT)为评估复杂系统的信息架构提供了一种新方法。在本研究中,我们描述了将 SIT 应用于动态问题所需的步骤。通过将SIT应用于动态问题所需的步骤。我们的路线图有四个步骤:(1)将动态系统分离为人-环境子系统;(2)选择适当的粗粒度并量化相关性;(3)确定可行性度量;(4)实施扰码协议并测量语义内容。我们将路线图应用到一个受癫痫发作神经动力学启发的模型中,在该模型中,代理(控制过程)试图将环境(基础过程)维持在非同步状态。同步动力学通过著名的仓本相位同步模型进行研究。我们将 SIT 应用于这一问题,揭示了语义信息和仓本模型的新特征。对于后者,我们发现将代理和环境(振荡器)的相关结构衔接起来,可以将模型置于一个新颖的计算(信息论)视角中,代理-环境动力学可以被视为对通信通道的分析。对于前者,我们发现我们系统中的所有信息都是语义信息。这与之前对觅食者进行的 SIT 研究形成了鲜明对比,在之前的研究中,我们看到了语义阈值,超过这个阈值就无法获得更多语义内容。
Semantic Information Theory in a feedback-control Kuramoto Model
Semantic Information Theory (SIT) offers a new approach to evaluating the
information architecture of complex systems. In this study we describe the
steps required to {\it operationalize} SIT via its application to dynamical
problems. Our road map has four steps: (1) separating the dynamical system into
agent-environment sub-systems; (2) choosing an appropriate coarse graining and
quantifying correlations; (3) identifying a measure of viability; (4)
implementing a scrambling protocol and measuring the semantic content. We apply
the road map to a model inspired by the neural dynamics of epileptic seizures
whereby an agent (a control process) attempts to maintain an environment (a
base process) in a desynchronized state. The synchronization dynamics is
studied through the well-known Kuramoto model of phase synchronization. Our
application of SIT to this problem reveals new features of both semantic
information and the Kuramoto model. For the latter we find articulating the
correlational structure for agent and environment(the oscillators), allows us
to cast the model in in a novel computational (information theoretic)
perspective, where the agent-environment dynamics can be thought of as
analyzing a communication channel. For the former we find that all the
information in our system is semantic. This is in contrast to previous SIT
studies of foragers in which semantic thresholds where seen above which no
further semantic content was obtained.