Complete functional characterization of sensory neurons by system identification

Michael C.K. Wu, Stephen V. David, Jack L. Gallant

Research output: Chapter in Book/Report/Conference proceedingChapter

214 Scopus citations


System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.

Original languageEnglish (US)
Title of host publicationAnnual Review of Neuroscience
EditorsSteven Hyman, Thomas Jessell, Charles Stevens
Number of pages29
StatePublished - 2006
Externally publishedYes

Publication series

NameAnnual Review of Neuroscience
ISSN (Print)0147-006X


  • Maximum a posteriori estimate
  • Nonlinear functional model
  • Prediction
  • Receptive field
  • Regression
  • Reverse correlation

ASJC Scopus subject areas

  • Neuroscience(all)


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