Temporal Dynamics of Neural Responses in Human Visual Cortex

Neural responses to visual stimuli exhibit complex temporal dynamics, including subadditive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic recordings from patients presented with stimuli that varied in duration, interstimulus interval (ISI) and contrast. Aggregating data across patients from both sexes yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several nonlinear features. Peak response amplitude saturates with high contrast and longer stimulus durations, the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs, and response latency decreases with increasing contrast. These features are accurately captured by a computational model composed of a small set of canonical neuronal operations, that is, linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptationrelated response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex and demonstrate that a simple model captures these dynamics at millisecond resolution.

Automated summary

Neural responses to visual stimuli exhibit complex temporal dynamics, which can be captured by a computational model. This study demonstrates the integration of various temporal features, such as subadditive temporal summation, adaptation, and slower dynamics at low contrast, within the same experiment. The model accurately captures the temporal dynamics in different regions of the visual cortex.