Abstract
Based on the energy model for disparity-tuned neurons, we calculate probability density functions of complex cell activity for random-dot stimuli. We investigate the effects of normalization and give analytical expressions for the disparity tuning curve and its variance. We show that while normalized and non-normalized complex cells have similar tuning curves, the variance is significantly lower for normalized complex cells, which makes disparity estimation more reliable. The results of the analytical calculations are compared to computer simulations.
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Stürzl, W., Mallot, H.A., Knoll, A. (2007). An Analytical Model of Divisive Normalization in Disparity-Tuned Complex Cells. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_79
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DOI: https://doi.org/10.1007/978-3-540-74690-4_79
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