Cross-Category Invariant Neurons in the Human Amygdala
Abstract
SUMMARY Recognizing individuals or objects across different contexts is a hallmark of primate cognition. Beyond recognition, the brain organizes visual stimuli into meaningful categories, supporting efficient perception and adaptive behavior. Previous studies in the human temporal lobe have identified concept cells supporting invariant representations of specific stimuli. However, invariant coding alone cannot account for how categorical knowledge is structured across related visual domains. Here, we analyzed single-neuron recordings from the human amygdala during the presentation of familiar visual stimuli spanning multiple categories. Using a novel data-driven analytic framework that integrates supervised population decoding with single-neuron analyses, we identified a population of cross-category invariant neurons. These neurons preserved exemplar-invariant responses while generalizing across multiple categories linked by shared naturalistic or human/domestic contexts. Our findings demonstrate that the human amygdala supports both invariant and associative forms of categorical representations. By linking stable identity representations across contextually related categories, the amygdala provides a flexible neural substrate for high-level visual organization, supporting efficient perception and adaptive behavior in complex environments. Highlights Human amygdala neurons encode invariant category representations at the single-cell level Category representations are organized according to naturalistic and social contexts Cross-category invariant neurons bridge concept-cell and mixed-selectivity coding Category encoding relies on opponent patterns of enhancement and suppression across domain
Fields of Study & Subjects
Memory and Neural Mechanisms
Face Recognition and Perception
Visual perception and processing mechanisms
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publication date
- 29 January 2026
- Document type
- Preprint