Comparing integrate-and-fire-like models estimated using
intracellular and extracellular data.
Neurocomputing 65: 379-385.
Presented at
Computational Neuroscience 2004, Baltimore, MD
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.
Reprint (500K,
pdf) | Related
work on estimation of neural models | on integrate-and-fire
models | Liam Paninski's
research