Entropy production of active matter systems as indicator for computing performance

What the math gives to ML

The paper identifies entropy production as a quantitative indicator of computational performance in driven reservoirs. Its transferable mechanism is the joint use of driven response and the discrepancy between driven and innate entropy production, rather than maximizing dissipation alone. A neural analogue can tune a recurrent reservoir by scanning recurrent gain or input coupling and estimating phase-space contraction, stochastic path irreversibility, and hidden-state response. The prediction is that the combined score peaks near the regime with the best validation accuracy, while weakly driven and excessively unstable regimes perform worse.