Two baseline responses give at most 36 ribbon kinetic candidates with constant reserve, shared unknown sensor filter, and no cancellations

Can two release baselines separate vesicle pool kinetics from unknown sensor dynamics? We prove a conditional finite identification result. The model has two active pools, positive rates, and a constant reserve. The same rates and unknown linear sensor filter apply at both steady baselines. A response quotient removes the filter. If no factor cancels, it gives at most 36 kinetic parameter candidates. A complete factor search finds all candidates in exact arithmetic. Five exact complex frequencies suffice to determine the quotient. A single retained candidate fixes the active pool capacity ratio. Absolute capacities remain free. In 1,000 synthetic models, exact quotient coefficients gave one candidate in every case. The largest relative ratio error was 6.42e-10. A double precision inverse from five frequencies accepted 893 models and failed its consistency check in 107, without added noise. A later 17-frequency check accepted 952 at the same tolerance. A second baseline separated the prior sensor counterexample. Local linear noise calculations showed weak information for nearby baselines and for one source model setting. Separate state-space calculations, nonlinear integration, exact rational checks, source hashes, and a clean repeat checked the work. The result is a method for the stated model. It does not prove general uniqueness, identify animal pool sizes, or cover changing reserves or changing sensor filters. The package preserves code, proof, all synthetic measurements, factor checks, figures, provenance, and repeat steps.

ribbon synapse · vesicle pools · structural identifiability · sensor dynamics · frequency response · multiple baselines · synaptic transmission · experimental design