Comparing integrate-and-fire models estimated using intracellular and extracellular data

Authors:
Liam Paninski;Jonathan Pillow;Eero Simoncelli
Affiliations:
Gatsby Computational Neuroscience Unit, University College London, UK and Howard Hughes Medical Institute, Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, U ...;Howard Hughes Medical Institute, Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA;Howard Hughes Medical Institute, Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA
Venue:
Neurocomputing
Year:
2005

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Abstract

We have recently developed a maximum-likelihood (ML) method for estimating integrate-and-fire-based stimulus encoding models that can be used even when only extracellular spike train data is available. Here we derive the ML estimator given the full intracellular voltage trace and apply both the extracellular-only and intracellular method to responses recorded in vitro, allowing a direct comparison of the model fits within a unified statistical framework. Both models are able to capture the behavior of these cells under dynamic stimulus conditions to a high degree of temporal precision, although we observe significant differences in the stochastic behavior of the two models.