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Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

Fig 4

Alternative to fitting: algorithmic features.

A) We describe all tuning curve properties of interest by algorithmically extracted features, as opposed to model parameters. The panel gives pseudocode for three selected features, illustrated also in panels B,C in corresponding colors. B) Feature extraction algorithms take as input only the sampled fitted tuning curve. Features are thus defined independent of a model function, allowing for their comparison between models. Furthermore, all aspects of tuning curves can be described by suitably chosen features such as Maximumleft (dark red), InnerMinimum (orange), Maximumright (blue), (occuring at positions MaximumAngleleft, InnerMinimumAngle and MaximumAngleright, respectively), InnerWidthleft (pink), InnerWidthright (green). C) Due to their algorithmic nature feature extraction rules can equally well be applied directly to the coarse measured trial-averaged tuning curve. Thereby, tuning curve properties can be described and analyzed without referring to the fit.

Fig 4