Topic: localized-phase-invariant model

To decipher the algorithm of perception, it is important to characterize neural tuning functions and identify the directions of maximal sensitivity and invariance to stimulus features. To this end, parametric stimuli are typically used, but they make strong assumptions about the stimulus features to which neurons are selective and invariant. Recently, the “inception loop” paradigm was developed, which combines large-scale neuronal recordings with deep learning system identification models which are trained to predict the responses of neurons to arbitrary stimuli. Inception loops have been used