Nonlinear circuits for naturalistic visual motion estimation

James E. Fitzgerald*, Damon A. Clark

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila’s visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly’s motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

Original languageEnglish (US)
Article numbere09123
JournaleLife
Volume4
Issue numberOCTOBER2015
DOIs
StatePublished - Oct 24 2015

ASJC Scopus subject areas

  • General Immunology and Microbiology
  • General Biochemistry, Genetics and Molecular Biology
  • General Neuroscience

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