# Two-State Dynamical Synapse

Equilibrium Propagation (EqProp) updates each synapse by comparing a local quantity at the free and nudged equilibria, which normally requires extra memory or a duplicated network to hold the earlier value. Here that temporal comparison is carried by the synapse's own two-timescale dynamics: a volatile state relaxes toward the product of pre- and postsynaptic activities while a non-volatile state stores the learned parameter, and the volatile state's change is transferred to the non-volatile one through a global gate. On MNIST with a single-hidden-layer network, the phase-based and continual forms reach test errors of 2.01% and 2.24%, against 2.00% and 2.28% for standard EqProp, within one standard deviation.

Equilibrium Propagation (EqProp) updates each synapse by comparing a local quantity at the free and nudged equilibria, which normally requires extra memory or a duplicated network to hold the earlier value. Here that temporal comparison is carried by the synapse's own two-timescale dynamics: a volatile state relaxes toward the product of pre- and postsynaptic activities while a non-volatile state stores the learned parameter, and the volatile state's change is transferred to the non-volatile one through a global gate. On MNIST with a single-hidden-layer network, the phase-based and continual forms reach test errors of 2.01% and 2.24%, against 2.00% and 2.28% for standard EqProp, within one standard deviation.