AINeutralarXiv – CS AI · 6h ago6/10
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Between Amnesia and Chaos: A Memory Stability Expressivity Trilemma for Trainable Dissipative Oscillator Networks
Researchers demonstrate that training physical neural networks composed of nonlinear oscillators reveals a fundamental tradeoff: memory capacity, gradient stability, and dynamical expressivity cannot be simultaneously optimized because all three are governed by damping parameters. Empirical validation on a twenty-oscillator network confirms theoretical predictions, showing trained substrates outperform frozen ones only within a narrow optimal band that contracts as memory horizons increase.