Dynamic Engagement of Human Motion Detectors across Space-Time Coordinates

Peter Neri

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Motion detection is a fundamental property of the visual system. The gold standard for studying and understanding this function is the motion energy model. This computational tool relies on spatiotemporally selective filters that capture the change in spatial position over time afforded by moving objects. Although the filters are defined in space-time, their human counterparts have never been studied in their native spatiotemporal space but rather in the corresponding frequency domain. When this frequency description is back-projected to spatiotemporal description, not all characteristics of the underlying process are retained, leaving open the possibility that important properties of human motion detection may have remained unexplored. We derived descriptors of motion detectors in native space-time, and discovered a large unexpected dynamic structure involving a >2× change in detector amplitude over the first ∼100 ms. This property is not predicted by the energy model, generalizes across the visual field, and is robust to adaptation; however, it is silenced by surround inhibition and is contrast dependent. We account for all results by extending the motion energy model to incorporate a small network that supports feedforward spread of activation along the motion trajectory via a simple gain-control circuit.

Original languageEnglish
Pages (from-to)8449-8461
Number of pages13
JournalJournal of Neuroscience
Volume34
Issue number25
DOIs
Publication statusPublished - 18 Jun 2014

Keywords

  • delayed feedback
  • extrapolation mechanism
  • gain control
  • kernel estimation
  • noise image classification
  • sequential recruitment

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