Between Amnesia and Chaos: A Memory Stability Expressivity Trilemma for Trainable Dissipative Oscillator Networks
arXiv:2606.09929v1 Announce Type: new Abstract: Physical reservoir computing harnesses nonlinear mechanical dynamics but, by convention, freezes the substrate and trains only a linear readout, presuming the substrate is not usefully trainable. We revisit that premise for networks of nonlinear oscillators whose mass, damping, and stiffness are learned end-to-end through a symplectic integrator. Our central result is a trilemma: memory horizon, gradient stability, and dynamical expressivity cannot be simultaneously maximized, because all three are governed by the damping. The backward gradient d
This paper re-evaluates fundamental assumptions in physical reservoir computing, driven by ongoing research into more energy-efficient and dynamic AI architectures.
Advanced concepts in trainable physical systems could lead to more efficient and capable AI hardware, impacting the future of computing and AI development.
The potential to train the physical substrate of reservoir computers suggests a departure from current frozen substrate paradigms, opening new avenues for hardware-aware AI design.
- · AI hardware developers
- · Materials science researchers
- · Edge AI computing
- · AI compute infrastructure
- · Traditional fixed-substrate reservoir computing
- · Hardware-agnostic AI model developers
Research accelerates into trainable physical substrates for AI, moving beyond purely software-defined models.
New classes of energy-efficient and specialized AI processors emerge, optimized for dynamic, dissipative systems.
The trilemma of memory, gradient stability, and expressivity becomes a central design constraint for future 'wetware' or bio-inspired computing systems.
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