Bayesian methods for event analysis of intracellular currents

TL;DR

This paper introduces a Bayesian method for analyzing intracellular currents that improves detection sensitivity and robustness, especially in noisy and multimodal experimental data, enhancing neural circuit analysis.

Contribution

The paper presents a novel Bayesian framework for detecting and characterizing postsynaptic currents, extending to multimodal data integration and network-level analysis.

Findings

Higher sensitivity in detecting small signals

Increased robustness to noise compared to standard methods

Effective integration of electrophysiological and optical data

Abstract

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.