Analog Dopplegangers: Twinning with Deep Continuous-Time Recurrent Neural Networks

Digital computers are built of analog circuit components that "twin" conceptual digital entities such as switches or logic gates. Employing such "analog doppelgangers" to twin conceptual discrete components modulo thresholding and clocking constraints was a necessary step in implementing modern digital computer. These digital computers have gone on to be so successful that in many circles the word "digital" has almost become synonymous with "computational". We may see some of this effect even the term "digital twin". This paper speculatively addresses the idea that the twin need not be digital in the strictest sense so long as it actually models, indistinguishably, the behavior of some other system. We will draw from previous work using fully-recurrent continuous time and continuous valued neural networks to twin optimal controllers for a simplified legged-locomotion agent. We will then argue that the use of "analog doppelgangers" to twin systems directly has benefits and utility that strictly digital twins may not possess in equal measure. Finally, we will examine and discuss what types of practical problems might be positively impacted by this alternative view.