title: Bayesian methods for event analysis of intracellular currents.
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 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.
  
  
  
  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.
authors.affiliation: Neurobiology and Behavior Program, Columbia University, United States; Center for Theoretical Neuroscience, Columbia University, United States. Electronic address: jsmerel@gmail.com.
authors.forename: Josh
authors.initials: J
authors.lastname: Merel
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, United States.
authors.forename: Ben
authors.initials: B
authors.lastname: Shababo
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, United States.
authors.forename: Alex
authors.initials: A
authors.lastname: Naka
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, United States; Department of Molecular and Cellular Biology, University of California, Berkeley, United States.
authors.forename: Hillel
authors.initials: H
authors.lastname: Adesnik
authors.affiliation: 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.
authors.forename: Liam
authors.initials: L
authors.lastname: Paninski
mesh_terms: D000465:Algorithms; D000818:Animals; D057205:Automation, Laboratory / Q000379:methods*; D001499:Bayes Theorem; D002118:Calcium / Q000378:metabolism; D003198:Computer Simulation; D042541:Intracellular Space / Q000502:physiology; D008822:Mice, Transgenic; D062308:Optogenetics / Q000379:methods; D018408:Patch-Clamp Techniques / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013569:Synapses / Q000502:physiology*; D055366:Synaptic Potentials / Q000502:physiology; D046509:Tissue Culture Techniques; D056969:Voltage-Sensitive Dye Imaging / Q000379:methods
chemical_list: D002118:Calcium
keywords: Bayesian methods; Calcium imaging; Connectivity mapping; Event detection; MCMC; Postsynaptic current