Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization

Abstract

Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recordings, has the potential to map monosynaptic connectivity at an unprecedented scale. However, optogenetic mapping of nearby connections poses a challenge, due to stimulation artifacts. When the postsynaptic cell expresses opsin, optical excitation can directly induce current in the patched cell, confounding connectivity measurements. This problem is most severe in nearby cell pairs, where synaptic connectivity is often strongest. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are consistent across repeated stimulations at similar laser power. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available at https://github.com/bantin/PhoRC.