Precise calcium-to-spike inference using biophysical generative models

The intramolecular dynamics of fluorescent indicators of neural activity can distort the accurate estimate of action potential ("spike") times. In order to develop a more accurate spike inference algorithm we characterized the kinetic responses to calcium of three popular indicator proteins, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow and brain slice recordings. jGCaMP8f showed a use-dependent slowing of fluorescence responses that caused existing inference methods to generate numerous false positives. From these data we developed a multistate model of GCaMP and used it to create Bayesian Sequential Monte Carlo (Biophys_SMC) and machine learning (Biophys_ML) inference methods that reduced false positives substantially. This biophysical method dramatically improved spike time accuracy, detecting individual spikes with a median uncertainty of 4 milliseconds, a performance level that reached the theoretical limit and is twice as accurate as any previous method. Our framework thus highlights advantages of physical model-based approaches over model-free algorithms.