Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment

Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding.

Our brains are as unique as our fingerprints, with significant differences in structure and neuronal activity from person to person. Yet mysteriously, these diverse brain circuits produce remarkably similar functions across individuals. This presents a fascinating paradox: how can such different neural systems create such consistent outputs?Our research tackles this problem by developing a new computational method called PNBA (Probabilistic Neural-Behavioral Representation Alignment). This approach helps us detect shared patterns in neural activity across different individuals, despite variations that occur naturally over time and between subjects.Using this approach, we discovered that the low-dimensional representations of neural population activity are remarkably preserved across individuals in both monkey and mouse brain regions, including those cortices responsible for movement and vision. This finding not only resolves the paradox of how structurally different brains can produce consistent behaviors but also suggests potential applications for brain-computer interfaces that may require less individual calibration, which could improve the development of neural technologies.