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

Liam Paninski, Jonathan W. Pillow and Eero P. Simoncelli
Neurocomputing 65:379-385. (2005)

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 MLestimator 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.

  • estimating IF model from extracellular data: paninski-NC-04
  • extracellular model applied to macaque retinal ganglion cell responses: pillow-JN-05
online publications