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John A. Perrone; Economy of scale: A motion sensor with variable speed tuning. Journal of Vision 2005;5(1):3. doi: https://doi.org/10.1167/5.1.3.
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© ARVO (1962-2015); The Authors (2016-present)
We have previously presented a model of how neurons in the primate middle temporal (MT/V5) area can develop selectivity for image speed by using common properties of the V1 neurons that precede them in the visual motion pathway (J. A. Perrone & A. Thiele,2002). The motion sensor developed in this model is based on two broad classes of V1 complex neurons (sustained and transient). The S-type neuron has low-pass temporal frequency tuning,p(ω), and the T-type has band-pass temporal frequency tuning,m(ω). The outputs from the S and T neurons are combined in a special way (weighted intersection mechanism [WIM]) to generate a sensor tuned to a particular speed,ν. Here I go on to show that if the S and T temporal frequency tuning functions have a particular form (i.e.,p(ω)/m(ω) =k/ω), then a motion sensor with variable speed tuning can be generated from just two V1 neurons. A simple scaling of the S- or T-type neuron output before it is incorporated into the WIM model produces a motion sensor that can be tuned to a wide continuous range of optimal speeds.
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