Mohanta, R. | bioRxiv Science

bioRxiv2025

A reference brain for the clonal raider ant

Ants exhibit remarkable collective and social behaviors, such as alloparental care1, chemical communication2, homing3, and cooperative group hygiene4. The clonal raider ant Ooceraea biroi is especially well-suited for investigating the neuronal and genetic underpinnings of these behaviors5. Unlike most ant species, O. biroi lacks a queen caste. Instead, colonies consist entirely of regular workers and slightly larger intercaste workers6. All workers reproduce in synchrony via parthenogenesis, giving rise to age-matched cohorts of clonally identical offspring7,8. This unique life history enables precise experimental control over age, genotype, and colony composition. These features have also facilitated the introduction of genetically encoded calcium indicators into O. biroi, enabling in vivo two-photon imaging to investigate the neural basis of social behaviors9. Despite its promise as a neuroscience model, the structure of the clonal raider ant brain has not been systematically characterized, and a representative reference brain does not exist. To address this gap, we imaged the brains of 40 age-matched, genetically identical individuals with confocal microscopy and, using 3D groupwise registration, generated the first reference brain for the species. We introduce a registration pipeline to align brains to this reference, facilitating the comparison of anatomical features across labeling experiments with high spatial precision. Unexpectedly, despite homogeneity in genotype, age, and external morphology, we discovered extensive interindividual variability across our collection of brain samples. This raises the possibility that behavioral division of labor in O. biroi is linked to individual differences in brain structure. This work provides a powerful resource for the emerging clonal raider ant neuroscience community and reveals novel features of the species neurobiology that may influence social behaviors and colony function.

neuroscience↗

bioRxiv2025

Discovering Symbolic Cognitive Models from Human and Animal Behavior

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.

animal behavior and cognition↗