Analyzing neural responses with vector fields

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Analyzing changes in the shape and scale of single cell response fields is a key component of many neurophysiological studies. Typical analyses of shape change involve correlating firing rates between experimental conditions or " cross-correlating" single cell tuning curves by shifting them with respect to one another and correlating the overlapping data. Such shifting results in a loss of data, making interpretation of the resulting correlation coefficients problematic. The problem is particularly acute for two dimensional response fields, which require shifting along two axes. Here, an alternative method for quantifying response field shape and scale based on correlation of vector field representations is introduced. The merits and limitations of the methods are illustrated using both simulated and experimental data. It is shown that vector correlation provides more information on response field changes than scalar correlation without requiring field shifting and concomitant data loss. An extension of this vector field approach is also demonstrated which can be used to identify the manner in which experimental variables are encoded in studies of neural reference frames.

Original languageEnglish (US)
Pages (from-to)109-117
Number of pages9
JournalJournal of Neuroscience Methods
Volume197
Issue number1
DOIs
StatePublished - Apr 15 2011

Keywords

  • Gradients
  • Movement field
  • Receptive field
  • Reference frames
  • Separability
  • Vector correlation

ASJC Scopus subject areas

  • Neuroscience(all)

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