The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves

Frontiers in Computational Neuroscience
Stuart Yarrow, Peggy Seriès

Abstract

Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher inf...Continue Reading

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Citations

Feb 23, 2017·Journal of Neural Engineering·Jan Saputra MüllerKlaus-Robert Müller
Mar 9, 2017·Frontiers in Computational Neuroscience·Fang HanHong Fan
Mar 7, 2020·Frontiers in Computational Neuroscience·Florian MeierAngelika Steger
Dec 18, 2020··Zaidao MeiZiyi Zhao

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