Figure 5.

Activity sensors provide more information than activity characteristics such as bursting duty cycle, and activation and inactivation variables improve the estimation from sensors. A, Comparison of estimation success rates obtained from various activity characteristics compared with the success achieved by using activity sensors (#87; for parameters, see supplemental Table S2, available at www.jneurosci.org as supplemental material) in each model cell. all char., Estimation done with all characteristics. 50% success indicates estimation at chance level. B, Comparison of success obtained from Ca²⁺-related quantities and different types of sensors. The best activation-only sensor (#365) is inferior to the best inactivating sensor (#87). C, In the normalized frequency of success obtained from the 366 different calcium sensors tested at the same time in all three model cells, non-inactivating sensors (n = 36) were inferior to inactivating sensors (n = 330). The maximum estimation success reached 86.40%. Success rates were collected in 50 bins for inactivating and in 10 bins for the non-inactivating sensors. D, Using minimum and maximum values of a single (activating and inactivating) sensor in addition to its average increased the estimation success to 87.46%. E, Using the same fast, slow, and DC (FSD) sensors in all model cells (see Materials and Methods) yielded 85,750 combinations of the FSD sensors from which we found a maximum estimation success of 88.17%. Histograms contain 50 bins in last two panels.