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

Liam Paninski, Jonathan William Pillow, Eero Simoncelli

Research output: Contribution to journalArticle

26 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)379-385
Number of pages7
JournalNeurocomputing
Volume65-66
Issue numberSPEC. ISS.
DOIs
StatePublished - Jun 1 2005

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Integrate-and-fire
  • Noise
  • Stimulus-response encoding

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