Abstract: Comparing integrate-and-fire-like models given intracellular and extracellular data

Comparing integrate-and-fire-like models given intracellular and extracellular data

Liam Paninski , Jonathan W Pillow , and Eero P Simoncelli

Presented at:

Computational Neuroscience Meeting (CNS*04), Baltimore Maryland, 18-20 July 2004.

Published in:

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.

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