A new photonic framework is proposed in which local learning rules, tunable memory, and adaptive dynamics arise intrinsically from driven-dissipative nonlinear optics, shown through numerical simulations.
Neural network weights do not converge to stationary points: An invariant measure perspective
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.optics 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
A new photonic framework is proposed in which local learning rules, tunable memory, and adaptive dynamics arise intrinsically from driven-dissipative nonlinear optics, shown through numerical simulations.