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Kedarnath P. Vilankar, David J. Field; Selectivity, hyperselectivity, and the tuning of V1 neurons. Journal of Vision 2017;17(9):9. doi: https://doi.org/10.1167/17.9.9.
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© ARVO (1962-2015); The Authors (2016-present)
In this article, we explore two forms of selectivity in sensory neurons. The first we call classic selectivity, referring to the stimulus that optimally stimulates a neuron. If a neuron is linear, then this stimulus can be determined by measuring the response to an orthonormal basis set (the receptive field). The second type of selectivity we call hyperselectivity; it is either implicitly or explicitly a component of several models including sparse coding, gain control, and some linear-nonlinear models. Hyperselectivity is unrelated to the stimulus that maximizes the response. Rather, it refers to the drop-off in response around that optimal stimulus. We contrast various models that produce hyperselectivity by comparing the way each model curves the iso-response surfaces of each neuron. We demonstrate that traditional sparse coding produces such curvature and increases with increasing degrees of overcompleteness. We demonstrate that this curvature produces a systematic misestimation of the optimal stimulus when the neuron's receptive field is measured with spots or gratings. We also show that this curvature allows for two apparently paradoxical results. First, it allows a neuron to be very narrowly tuned (hyperselective) to a broadband stimulus. Second, it allows neurons to break the Gabor–Heisenberg limit in their localization in space and frequency. Finally, we argue that although gain-control models, some linear-nonlinear models, and sparse coding have much in common, we believe that this approach to hyperselectivity provides a deeper understanding of why these nonlinearities are present in the early visual system.
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