Cross-individual translation of spontaneous zebrafish brain activity through a shared latent representation

Abstract Spontaneous activity is a hallmark of brain function, reflecting the underlying circuit organization. Identifying conserved structure across individuals in this self-sustained activity has remained a longstanding challenge, especially in vertebrates where one-to-one neuron correspondence is inaccessible. Here, we introduce latent-aligned Restricted Boltzmann Machines (LaRBMs), an unsupervised generative approach that uncovers a common representational space from cell-resolved whole-brain recordings in larval zebrafish. This latent space consists of spatially localized co-activation motifs, or cell assemblies, that generalize across animals and form interpretable building blocks of population-wide activity. LaRBMs enable bidirectional mapping of instantaneous whole-brain activity patterns between individuals: activity patterns from one fish can be encoded into the latent space and decoded into another. The translated patterns are assigned high probability by the recipient model and retain the original spatial organization. These results show that spontaneous activity in the vertebrate brain is highly stereotyped at the level of functional cell assemblies and can be reliably captured through a common latent code. Because it provides an interpretable and quantitative framework for functional cross-individual alignment, LaRBM paves the way for comparative phenotyping of brain activity across developmental, genetic, and pathological variation. Significance Statement Spontaneous brain activity, without external stimuli, shapes development, constrains coding, and reflects neural organization. Whether this activity reveals similar organization across individuals has been unclear. Using single-cell, whole-brain recordings in larval zebrafish and statistical learning, we find that spontaneous dynamics share a common structure across animals. Spatially organized co-activated neuron assemblies recur across individuals and act as building blocks of population activity. This shared representation enables fictive translation of activity from one fish into another’s neural space while preserving spatial and statistical plausibility. These results suggest that brains organize population activity by similar principles to represent internal states. Our findings reveal conserved organization in the vertebrate brain and establish a quantitative framework for comparing brain dynamics across individuals and conditions. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵* georges.debregeas{at}sorbonne-universite.fr This revised version more carefully defines key concepts, and clarifies the rationale for model hyperparameters. It also adds several new analyses, including a leave-one-fish-out validation of the voxelized global RBM, a systematic hyperparameter sensitivity analysis for the number of hidden units and regularization strength, and new controls showing enhanced cross-fish alignment in hidden space and comparable free-energy structure for translated activity patterns. The discussion has also been extended. The main results and conclusions remain unchanged.