Toward a formal theory for computing machines made out of whatever physics offers

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures. Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

Automated summary

Approaching limitations of digital computing technologies have led to the exploration of neuromorphic and other unconventional computing methods. A formal theory beyond the classical symbolic-algorithmic Turing machine theory is needed to systematically engineer unconventional computing systems. This article proposes a general strategy for developing such a theory, particularly focusing on a bottom-up approach called fluent computing, which models physical computing systems based on what can be measured. The authors emphasize the challenges in learning from human brains to create powerful computers due to the absence of a guiding computing theory.