Defining the Resolution of Optogenetic Circuit Mapping

• Activity Number: 271 - Statistical Analysis of Complex Imaging Data
• Type: Invited
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Shizhe Chen* and Liam Paninski and Ben Shababo and Hillel Adesnik
• Companies: University of California, Davis and Columbia University and University of California, Berkeley and University of California, Berkeley

Abstract

Circuit-mapping experiments combining whole-cell electrophysiology with two-photon optical stimulation of potentially presynaptic neurons have produced rich data on monosynaptic connectivity of neural circuits. However, mapping densely-packed presynaptic populations at cellular resolution has proven challenging, making the precise localization of connected neurons difficult. To interpret data resulting from these experiments, it is therefore critical to characterize the spatial resolution of stimulation. We develop a generative model with three main components: a neural response model which predicts presynaptic spike rates given the power and location of stimulation targets, a connectivity model which filters presynaptic spike rates into a postsynaptic event rate, and a postsynaptic model which converts the postsynaptic event rate into a voltage-clamp observation. We develop efficient online Bayesian variational inference methods for tracking the posterior of the model parameters given the observed data. We use simulated and real data to characterize the resulting resolution limits, and compare the accuracy of the proposed inference methods against simpler baseline approaches.