Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

Abstract

The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model's output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a secondorder RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multistage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales.

Short- and long-term forms of neural adaptation: An ERP investigation of dynamic motion aftereffects

“…They further reported that the first-order leaky integrator was sufficient to implement adaptation effects of long durations which can span many seconds. However, a second-order leaky integrator, which causes the sensor to require a finite amount of time to react to a sudden change in stimulation, was critical for the rapid form of MAE (Pavan, Contillo, & Mather, 2014). These findings clearly demonstrate that the neural mechanisms operating over different timescales can be supported and recruited in the same neural substrate.…”

“…These findings clearly demonstrate that the neural mechanisms operating over different timescales can be supported and recruited in the same neural substrate. According to Pavan et al (2014) and previous research (e.g., Wark, Fairhall, & Rieke, 2009), the temporal dynamics of adaptation may reflect a balance between adapting rapidly to avoid short-term saturation and adapting slowly (over longer timescales) to avoid instability in the absence of changes in image statistics. This is because changes in natural scenes occur over multiple Fig.…”