发表机构
Cortical Labs（Cortical Labs）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于信息学和算法热力学的类n体框架，引入信息焓势指标，结合神经临界性，解释大脑处理信息产生智能的机制，为自由能原理提供可证伪性。
AI 中文摘要
关于大脑如何处理信息并产生智能输出的理论众多，且往往难以得到确凿验证。本文采用基于信息学和算法热力学的方法，研究神经系统对不同信息模式的差异化响应，重点关注信号的信息熵与新提出的信息焓（information enthalpy，代表系统内可用于预测及其他形式工作的结构化信息内部资源）之间的关系。为评估外部信号提升系统信息焓的能力，本文引入信息焓势（information enthalpy potential, IEP）作为定义指标，提出并实现了一种量化给定信号在一系列示例信号中潜在信息焓（即IEP）的方法。本文推测神经系统如何在生物框架内处理信息焓，随后展示其如何整合现有方法（如自由能原理）并为这些方法提供可证伪性；该框架也结合了神经临界性的作用。最后，本文提出一个可测试、可证伪的框架，其中各驱动因素内的不同机制通过所描述的类n体模型相互作用，以调控神经系统在高维状态空间中的“运动”，这些特征被认为构成了神经驱动因素的最基础形式，催生了通常被称为“智能”的复杂适应性行为。
英文摘要
Theories of how the brain processes information and returns intelligent outputs are numerous and often difficult to conclusively test. Here, we consider an approach grounded in informatics and algorithmic thermodynamics for how neural systems respond to different patterns of information in different ways, with a focus on the relationship between the information entropy of a signal and a new proposed quantity we term information enthalpy, which represents the internal resource of structured information within a system available to be used for predictions and other forms of work. To evaluate the capacity of the external signals to increase information enthalpy in the system, we introduce the information enthalpy potential (IEP) as a defined metric. We propose and implement a method for quantifying the amount of potential information enthalpy - the IEP - in a given signal across a range of example signals. We offer a conjecture of how information enthalpy may be treated by neural systems within a biological framework before showing how it may integrate and offer falsifiability to existing approaches such as the Free Energy Principle. This framework is also positioned in light of the role of neural criticality. Finally, we postulate a testable and falsifiable framework where the distinct mechanisms within each driver interact through a described n-body-inspired model to govern the "motion" of a neural system through a high dimensional state-space. Through these processes, it is proposed that these features form the most fundamental basis of the neural drivers that give rise to the complexity of adaptive behaviors that are commonly called: intelligence.
