Temporal Dynamics of Neural Responses in Human Visual Cortex

Abstract

Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive 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 (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients 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 non-linear 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; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast-and adaptation-related 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.2

Mapping the Dynamics of Visual Feature Coding: Insights into Perception and Integration

“…Going further than previous studies, we were able to show how multiple features of the same stimulus are coded simultaneously and sustained for prolonged periods. This prolonged coding of each feature support prior research showing feature coding in multiple areas of the visual pathway beyond V1 (e.g., Desimone et al, 1985;Groen et al, 2022;Kamitani and Tong, 2005;Marquardt et al, 2018;Seymour et al, 2009). We found distinct representations for each feature, reflecting separable neural populations and/or different codes of multiplexing cells specialised for coding each feature.…”

Delayed normalization model captures disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

“…Delayed normalization model paired with different filter shapes can account for both near-additive and sub-additive temporal summation. In VSDI, the measured normalization filter (in the denominator) was biphasic, whereas the normalization filter used to account for ECoG data, which were substantially sub-additive as fMRI BOLD signals, was monophasic [16, 32]. In Figure 3C and 3D, we demonstrated that with different normalization filter shapes, the model could account for both near-additive and sub-additive temporal summations.…”

Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex

“…The dataset was collected as part of a larger project funded by the NIH (R01MH111417). The methods for collecting and preprocessing the visual ECoG data have been described recently (Groen et al, 2022). For convenience, data collection methods below duplicate some of the text from the methods section of Groen et al, (2022) with occasional modifications to reflect slight differences in participants, electrode selection, and pre-processing.…”

“…To correct the delay, UMCU data were aligned to the NYU data based on a cross- correlation on the average event-related potentials (ERPs) across all stimulus conditions from the V1 and V2 electrodes from three participants (1 UMCU, 2 NYU). The delay in stimulus presentation was estimated to be 72ms, and stimulus onsets of the UMCU participants were shifted accordingly (Groen et al 2022). One NYU patient (Patient 3) had inaccurate trigger onset signals for each stimulus event due to a recording malfunction.…”