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Sid Henriksen, Bruce Cumming, Jenny Read; Modeling response variability in disparity-selective cells. Journal of Vision 2017;17(10):755. doi: 10.1167/17.10.755.
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© ARVO (1962-2015); The Authors (2016-present)
Stereo vision relies on disparity-selective cells in primary visual cortex. The binocular energy model (BEM) has been successful in capturing a range of properties of these neurons. While the BEM's ability to capture mean rates have received substantial attention, little effort has been directed to understanding response variability. We have previously shown that the BEM predicts that the spike rate variance should increase with the square of the mean rate. Importantly, this variance is stimulus driven, reflecting the effect of different dot patterns while disparity is fixed. Recording from V1 neurons in the macaque, we used a two-pass method to separate stochastic variability ("internal variance") from stimulus induced variability ("external variance"). We found that V1 neurons show much less external variance than the BEM. This failure is partly due to the BEM's highly constrained structure. We fit more general linear-nonlinear (LN) models, with the number of subunits as a free parameter, to neuronal data. This general architecture is able to capture both the mean and variability of real cells much better. In particular, by incorporating multiple orthogonal excitatory subunits, the new model is able to achieve lower variability for a given mean than is possible in the BEM. However, problems remain. Notably, while the BEM produced too much variability, the new model produces too little. We show that while the new model captures the "internal" variability due to the spike generation process, it underestimates the "external" variability produced by different random noise patterns with a given correlation and disparity. The new model also still shows the characteristic relationship between the Fano Factor (Variance/Mean) and the mean which we reported previously in the BEM, and which is absent in real cells. Thus, this substantial generalization of the BEM is still not an accurate model of real V1 neurons
Meeting abstract presented at VSS 2017
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