  Investigation of neural circuit functioning often requires statistical
interpretation of events in subthreshold electrophysiological recordings. This
problem is non-trivial because recordings may have moderate levels of
structured noise and events may have distinct kinetics. In addition, novel
experimental designs that combine optical and electrophysiological methods will
depend upon statistical tools that combine multimodal data. We present a
Bayesian approach for inferring the timing, strength, and kinetics of
postsynaptic currents (PSCs) from voltage-clamp recordings on a per event
basis. The simple generative model for a single voltage-clamp recording
flexibly extends to include network-level structure to enable experiments
designed to probe synaptic connectivity. We validate the approach on simulated
and real data. We also demonstrate that extensions of the basic PSC detection
algorithm can handle recordings contaminated with optically evoked currents,
and we simulate a scenario in which calcium imaging observations, available for
a subset of neurons, can be fused with electrophysiological data to achieve
higher temporal resolution. We apply this approach to simulated and real ground
truth data to demonstrate its higher sensitivity in detecting small
signal-to-noise events and its increased robustness to noise compared to
standard methods for detecting PSCs. The new Bayesian event analysis approach
for electrophysiological recordings should allow for better estimation of
physiological parameters under more variable conditions and help support new
experimental designs for circuit mapping.
