Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment

TL;DR

This paper introduces PNBA, a probabilistic framework that aligns neural representations across individuals and species, revealing consistent neural coding despite biological heterogeneity, and enabling zero-shot behavior decoding.

Contribution

PNBA is the first probabilistic model that achieves hierarchical neural representation alignment across subjects and species with generative constraints.

Findings

Robust neural representations in monkey M1 and PMd identified.

Similar preservation observed in mouse V1.

Zero-shot behavior decoding enabled across cortices and species.

Abstract

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.