Temporal dynamics of short-term neural adaptation across human visual cortex

Area of Science:

• Neuroscience
• Computational Neuroscience
• Visual Perception

Background:

• Neural adaptation is a fundamental property of the visual cortex, but its hierarchical organization and computational underpinnings remain incompletely understood.
• Previous research indicates adaptation occurs throughout the visual cortex, yet specific differences in temporal dynamics and mechanisms across visual hierarchy are not well-defined.

Purpose of the Study:

• To characterize temporal adaptation signatures in the human visual cortex across different hierarchical levels.
• To investigate how adaptation patterns and computational mechanisms differ between early visual areas (V1-V3) and higher visual areas (ventral- and lateral-occipitotemporal cortex).
• To model neural adaptation using a computational framework that accounts for stimulus category and area-specific differences.

Main Methods:

• Analysis of time-varying intracranial electroencephalography (iEEG) data from participants viewing naturalistic image categories.
• Characterization of adaptation and recovery dynamics in response to varying stimulus duration and repetition intervals.
• Augmentation of a delayed divisive normalization (DN) model to incorporate stimulus category-specific input scaling for predicting neural responses.

Main Results:

• Ventral- and lateral-occipitotemporal cortex exhibited slower adaptation and prolonged recovery compared to V1-V3.
• Recovery from adaptation was slower for preferred stimuli than non-preferred stimuli in category-selective electrodes.
• The augmented DN model accurately predicted neural responses, with fits suggesting slower normalization dynamics in higher areas and category-selective input strength contribute to observed differences.

Conclusions:

• Systematic differences in temporal adaptation exist between lower and higher visual brain areas.
• A unified computational model of history-dependent normalization dynamics, parameterized by area, can explain these hierarchical differences in neural adaptation.
• Findings provide insights into the computational principles governing neural processing across the visual hierarchy.