Normalization Accounts for Temporal Dynamics in the Human Somatosensory Cortex

Abstract

Sensory processing is fundamentally shaped by stimulation history. For example, in the visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (six participants, all female) and intracranial electroencephalography (iEEG, two participants, both male) to measure responses to time-varying vibrotactile stimuli in the human somatosensory cortex. Stimuli consisted of single and paired pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced subadditive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time.