Unifying Temporal Phenomena in Human Visual Cortex

Abstract

Combining sensory inputs over time is fundamental to seeing. Due to temporal integration, we do not perceive the flicker in fluorescent lights nor the discrete sampling of movie frames; instead we see steady illumination and continuous motion. As a result of adaptation, elements of a scene that suddenly change in appearance are more salient than elements that do not. Here we investigated how the human nervous system combines visual information over time, measuring both functional MRI and intracortical EEG. We built predictive models using canonical neural computations, and account for temporal integration and adaptation. The models capture systematic differences in how information is combined in different visual areas, and generalize across instruments, subjects, and stimuli. AbstractThe visual system analyzes image properties across multiple spatial and temporal scales. Population receptive field ("pRF") models have successfully characterized spatial representations across the human visual pathways. Here, we studied temporal representations, measuring fMRI and electrocorticographic ("ECoG") responses in posterior, lateral, ventral, and dorsal visual areas to briefly viewed contrast patterns. We built a temporal pRF model employing linear summation and time-varying divisive normalization. Our model accurately predicts the fMRI amplitude and ECoG broadband timecourse, accounting for two phenomena -accumulation of stimulus information over time (summation), and response reduction with prolonged or repeated exposure (adaptation). We find systematic differences in these properties: summation periods are increasingly long and adaptation more pronounced in higher compared to earlier visual areas. We propose that several features of temporal responses -adaptation, summation, and the timescale of temporal dynamics -can be understood as resulting from a small number of canonical neuronal computations.

Differential sustained and transient temporal processing across visual streams

“…Notably, the estimated timing parameters from our experiments are largely consistent with parameters of neural IRFs derived from compressive temporal models applied to fMRI [6], as well ECoG and electrophysiology data [47], which have millisecond temporal resolution. Another aspect of our results shows that temporal parameters of neural responses vary across early and high-level areas in the visual processing hierarchy [1, 19–21].…”

“…This deviation from the hierarchical view may be due to the impact of additional factors on neural response latencies, which may also vary across areas. For example, the contrast of images may affect the time to peak in V1 more than in higher-level visual regions [47, 48].…”

Compressive Temporal Summation in Human Visual Cortex

“…If we assume that this includes a neurovascular compressive exponent of 0.5, then the stimulus-to-neural response would have exponents ranging from 0.2 (IPS) to 0.56 (V1), still highly compressive. This interpretation is supported by preliminary analyses of intracranial data, which show substantial temporal nonlinearities in the neural response (Zhou et al, 2017).…”

Temporal stability of stimulus representation increases along rodent visual cortical hierarchies

“…For instance, adaptation has been shown to depress visual cortical responses to repeated presentation of perceptually similar visual stimuli in both monkeys and humans [24][25][26] , resulting in reduced discriminability of the adapted stimuli 27 . fMRI evidence in humans suggests that the impact of adaptation mechanisms may increase along the ventral stream [28][29][30] and that neural responses in higher areas of the hierarchy may be driven more strongly by transient than sustained stimuli 30,31 . Similarly, in rats, adaptation has been shown to increase in magnitude along the cortical shape-processing hierarchy 32 , attenuating the responses to predictable stimuli 33 .…”