Models of neural computation: Difference between revisions

Revision as of 06:42, 12 October 2011

Revision as of 06:43, 12 October 2011

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The HR model predicts a peaking of the response at a particular input temporal frequency. The conceptually similar Barlow–Levick model is deficient in the sense that a stimulus presented to only one receptor of the pair is sufficient to generate a response. This is unlike the HR model, which requires two correlated signals delivered in a time ordered fashion. However the HR model does not show a saturation of response at high contrasts, which is observed in experiment. Extensions of the Barlow-Levick model can provide for this discrepancy<ref>Gonzalo G. de Polavieja, 2006. Neuronal Algorithms That Detect the Temporal Order of Events "Neural Computation" 18 (2006) 2102–2121</ref>.

====Watson-AhumadaWatson–Ahumada Modelmodel for Motionmotion Estimationestimation in Humanshumans====

This uses a cross-correlation in both the spatial and temporal directions, and is related to the concept of [[optical flow]].

<ref>Andrew B. Watson and Albert J. Ahumada, Jr., 1985. Model of human visual-motion sensing "J. Opt. Soc. Am. A" 2(2) 322–341</ref>