iModel
Related models

DS_Gabor_One
MEO_Gabor
ME_Gabor
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Variations

MEO_Gabor.sHtQ
MEO_Gabor.sHtH
MEO_Gabor.sHt1
MEO_Gabor.sHt2
MEO_Gabor.s1tQ
MEO_Gabor.s1tH
MEO_Gabor
MEO_Gabor.s1t2
MEO_Gabor.s2tQ
MEO_Gabor.s2tH
MEO_Gabor.s2t1
MEO_Gabor.s2t2

MEO_Gabor
Opponent Motion Energy, Gabor Filter
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Summary

References
  • Adelson EH, Bergen JR (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am A 2:284-299.

  • Bair W, Movshon JA (2004) Adaptive temporal integration of motion in direction-selective cells in macaque visual cortex. J Neurosci 24:7305--7323.

  • Grzywacz NM, Yuille AL (1990) A model for the estimate of local image velocity by cells in the visual cortex. Proc Roy Soc Lond B 239:129-161.

  • van Santen JPH, Sperling G (1984) Temporal covariance model of human motion perception. J Opt Soc Am A 1:451--473.

  • van Santen JPH, Sperling G (1985) Elaborated Reichardt detectors. J Opt Soc Am A 2:300--321.

MEO_Gabor

The visual stimulus is processed (convolved) by four linear Gabor filters (icons show x-t slices of 3D filters) to yield the signals fpe (filter preferred even), fpo (filter preferred odd), fae (anti-preferred even) and fao (anti-preferred odd). Each filter output is squared, and the signals are added for each quadrature pair to give the motion energy in the preferred direction, mep, and the anti-preferred direction, mea. These two oppositely tuned ME signals are subtracted to give the opponent motion energy, meopp.

The opponent signal (meopp) is then offset, scaled and half-wave rectified, and it is used to drive a Poisson spiking mechanism. The spikes are time shifted to simulate a neurobiological latency. See the model (.moo) files for the parameters that govern these computations.