Abstract
Extracellular recordings of neuronal cells are frequently a part of in vitro and in vivo experimental studies as a means of monitoring network-level dynamics. Their connections to intracellular dynamics are not well understood. Single-unit recordings are a more direct way to measure intracellular dynamics, but are typically difficult and expensive. On the other hand, simple differential equations models exist for single neurons. In this article, we apply a recent advance in data assimilation theory, designed to correct bias in general observation functions, toward the reconstruction of model-based intracellular dynamics from extracellular recordings.
Highlights
In vitro and in vivo neuronal experiments in the laboratory are frequently centered around measurements of cell potential
In the results presented below, we assume noisy extracellular data are available from a neuronal system and we implement an ensemble Kalman filter (EnKF) with a generic intracellular spiking model to reconstruct the intracellular state of the system
The successful implementation of data assimilation methods for estimation is dependent on an accurate mapping of the model state to the experimental measurements
Summary
In vitro and in vivo neuronal experiments in the laboratory are frequently centered around measurements of cell potential. The recorded extracellular potential is in general a complicated sum of spatially distributed currents [1] within a complex extracellular space [2]. There is a complicated relationship between the spatial location of the extracellular measurement with respect to the cell, resulting in different waveform properties based on the distance from the recording site to the neuron. This representative example demonstrates some of the differences between the two types of cell measurements
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
