Normalization by orientation-tuned surround in human V1-V3

• Published in: PLoS computational biology 2023-12, Vol.19 (12), p.e1011704-e1011704
• Main Authors: Fang, Zeming, Bloem, Ilona M, Olsson, Catherine, Ma, Wei Ji, Winawer, Jonathan
• Format: Article
• Language: English
• Subjects: Analysis; Biology and Life Sciences; Contours; Datasets; Energy; Engineering and Technology; Functional magnetic resonance imaging; Heterogeneity; Humans; Linear filters; Local population; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine and Health Sciences; Methods; Neurons; Neurons - physiology; Orientation - physiology; Orientation behavior; Photic Stimulation - methods; Physical Sciences; Quadratures; Research and Analysis Methods; Spatial distribution; Stimuli; Visual cortex; Visual Cortex - diagnostic imaging; Visual Cortex - physiology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011704

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a multi-stage computation. The first stage is linear filtering. The second stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of the linear filters in quadrature phase. The third stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet a traditional divisive normalization model predicts responses that are about the same. Motivated by these observations and others from the literature, we implement a divisive normalization model in which cells matched in orientation tuning ("tuned normalization") preferentially inhibit each other. We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We conclude that even in primary visual cortex, complex features of images such as the degree of heterogeneity, can have large effects on neural responses.