Josh Merel1, Ben Shababo2, Alex Naka2, Hillel Adesnik3, Liam Paninski4. 1. Neurobiology and Behavior Program, Columbia University, United States; Center for Theoretical Neuroscience, Columbia University, United States. Electronic address: jsmerel@gmail.com. 2. Helen Wills Neuroscience Institute, University of California, Berkeley, United States. 3. Helen Wills Neuroscience Institute, University of California, Berkeley, United States; Department of Molecular and Cellular Biology, University of California, Berkeley, United States. 4. Neurobiology and Behavior Program, Columbia University, United States; Center for Theoretical Neuroscience, Columbia University, United States; Department of Statistics, Columbia University, United States; Grossman Center for the Statistics of Mind, Columbia University, United States.
Abstract
BACKGROUND: 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. NEW METHOD: We present a Bayesian approach for inferring the timing, strength, and kinetics of post-synaptic currents (PSCs) from voltage-clamp electrophysiological recordings on a per event basis. The simple generative model for a single voltage-clamp recording flexibly extends to include additional structure to enable experiments designed to probe synaptic connectivity. RESULTS: 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. COMPARISON WITH EXISTING METHODS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. NEW METHOD: We present a Bayesian approach for inferring the timing, strength, and kinetics of post-synaptic currents (PSCs) from voltage-clamp electrophysiological recordings on a per event basis. The simple generative model for a single voltage-clamp recording flexibly extends to include additional structure to enable experiments designed to probe synaptic connectivity. RESULTS: 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. COMPARISON WITH EXISTING METHODS: 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. CONCLUSIONS: 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.
Authors: Alexander Naka; Julia Veit; Ben Shababo; Rebecca K Chance; Davide Risso; David Stafford; Benjamin Snyder; Andrew Egladyous; Desiree Chu; Savitha Sridharan; Daniel P Mossing; Liam Paninski; John Ngai; Hillel Adesnik Journal: Elife Date: 2019-03-18 Impact factor: 8.140
Authors: Pablo Rojas; Jenny A Plath; Julia Gestrich; Bharath Ananthasubramaniam; Martin E Garcia; Hanspeter Herzel; Monika Stengl Journal: Netw Neurosci Date: 2019-09-01