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• W2080346041 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." @default.
• W2080346041 created "2016-06-24" @default.
• W2080346041 creator A5039607589 @default.
• W2080346041 date "2014-06-18" @default.
• W2080346041 modified "2023-09-23" @default.
• W2080346041 title "Dynamic Engagement of Human Motion Detectors across Space-Time Coordinates" @default.
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• W2080346041 doi "https://doi.org/10.1523/jneurosci.5434-13.2014" @default.
• W2080346041 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4061388" @default.
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