# Reverse engineering generative models from zebrafish decision-making Presenter: Takuya Isomura Contribution: Livestreamed panel or group session Public ID: talk-1f31f2f439b815ce ## Submitted abstract Understanding biological intelligence requires identifying the variational invariant structures underlying neuronal dynamics. Experimental data provide access to the evolution of neural activity and synaptic weights. These dynamics can be expressed in different coordinates while preserving a common dynamical organisation. In Bayesian coordinates, they can be read as belief updating under an implicit generative model; in computational coordinates, as action generation, including behaviour, decision-making, and algorithmic computation. Triple equivalence formalises transformations among these descriptions and identifies their shared variational structure. The empirical question is which implicit generative model is instantiated by a given neuronal system. Here, reverse engineering was applied to neural and behavioural data from zebrafish learning a visually guided go/no-go decision-making task. From initial neural activity, the method identified individual-specific implicit generative models and instantiated them as synthetic agents that minimise variational free energy. These agents generated trial-by-trial predictions of subsequent synaptic plasticity, neural activity, and behavioural performance. The predictions captured individual learning trajectories and generalised to previously unseen task conditions. Their agreement with unseen empirical data provides a quantitative test of the predictive validity of the free-energy principle in behaving animals. More broadly, this framework shows how present neuronal states can be used to forecast future learning and self-organisation in individual biological systems. Source: [submitted source]() Abstract SHA256: 04388a871e9a24ae6600479de9c81b192924b8dea54bca793cac206d743cf80b ## Organizer review checklist - [ ] Confirm the submitted title, presenter, and abstract with the presenter. - [ ] Confirm contribution format and scheduling needs. - [ ] Confirm public permissions for any additional material before publication. - [ ] Prepare discussion questions grounded in the submitted abstract. ## Public learning resources These approved event resources are starting points; topic relevance and author identity have not been inferred. - Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology - Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations - GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER - Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Reverse%20engineering%20generative%20models%20from%20zebrafish%20decision-making