  The random motion of molecules in living cells has consistently been reported
to deviate from standard Brownian motion, a behavior coined as ``anomalous
diffusion''. Fluorescence Correlation Spectroscopy (FCS) is a powerful method
to quantify molecular motions in living cells but its application is limited to
a subset of random motions and to long acquisition times. Here, we propose a
new analysis approach that frees FCS of these limitations by using machine
learning to infer the underlying model of motion and estimate the motion
parameters. Using simulated FCS recordings, we show that this approach enlarges
the range of anomalous motions available in FCS. We further validate our
approach via experimental FCS recordings of calibrated fluorescent beads in
increasing concentrations of glycerol in water. Taken together, our approach
significantly augments the analysis power of FCS to capacities that are similar
to the best-in-class state-of-the-art algorithms for single-particle-tracking
experiments.
