Thermodynamic Intelligence: A Fully Analog Neural Network Based on the Information Field with Memristive Learning and Rigorous Mathematical Proofs
Abstract : This paper introduces a novel architecture for intelligent systems, grounded in the natural dynamics of the information field. In this approach, the fundamental concepts of computation and learning are realized not through digital instructions, but through the intrinsic, continuous behavior of a physical field. The processing elements of this architecture are designed to interact with one another and with the environment directly via the governing laws of that field, while the connections between them possess the ability to adapt and store information in a non‑volatile manner within their material structure. The presented mathematical framework provides rigorous proofs of stability, convergence of the learning process, and resilience against external perturbations and fabrication variations. Extensive numerical simulations at the circuit level confirm the correct operation of this architecture and demonstrate several orders of magnitude improvement in energy consumption compared to conventional digital systems. Furthermore, the paper explores deep conceptual links between the behavior of this system and certain observed natural phenomena, arguing that a unified set of physical principles may underlie intelligence across different substrates. Collectively, this work lays the foundation for a new generation of learning machines that speak directly in the language of nature.