The Virtual Brain Ontology
A structured knowledge base and toolset for reproducible brain network simulations
what is tvb-o
One file. Any backend. A whole brain.
Write a brain-network experiment down once; TVB-O compiles it to running code on any backend. Below is one real spec, 84 regions on a diffusion connectome, and 20 simulations of it.
the sweep · 20 real runs
mean FC +0.000
quietactive
Every slider position is an actual exp.run("tvboptim") on the spec at left. The same spec compiles to each backend beside it.
the platform
Browse the library, then build on it
Every model, network and study is curated from the literature, versioned with its provenance and validated against the schema — all of it open to explore. A free account adds a workspace on top, so what you build stays yours.
Counts from the current TVB-O database.
explore
Models, experiments and studies
Filter the curated database by class, search it by name, and open any record to its equations, parameters and the paper it came from.
- Follow the links between a study, its model and its network
- Open any record straight into the builder, or download it as YAML
- No account needed — every public record is readable
save · create · share
Your own models, kept under your name
Assemble an experiment in the browser — network, dynamics, integrator, coupling — and keep it in a workspace you control.
- Private by default, until you choose to share
- Share with a colleague or publish for peer review
- Load any saved experiment into Python with an API key
the community
Ask, and talk it through
Stuck on a model, a backend or a connectome? Ask in Help and get one accepted answer. For the open questions — methods, papers, where the field is going — there is Community.
the idea
One specification, two payoffs.
TVB-O encodes a whole brain-network simulation — dynamics, network, integration, observation and provenance — as one typed, ontology-grounded specification. From that single source it compiles runnable code and a methods section, and grounds every entity in a knowledge graph. Reachable by people and by AI agents.
The source
One typed object holds the whole experiment
Not just the model — the network it runs on, how it is integrated, what is observed, how it is explored and analysed, and who ran it. Every field is validated against the schema and linked to the ontology.
Two payoffs
The same specification is executable and comparable
The same typed file compiles to runnable code across backends and grounds into a searchable knowledge graph.
Compile to code and methods
One compiler emits equivalent code across independent backends and a publication-ready methods section — so a released study re-runs without drift between the paper and its implementation.
Ground into a knowledge graph
Every entity is grounded in four domains at once, so models that share no notation can still be compared, searched, and cited by a stable identifier.
Access
Three doors into the same knowledge
Knowledge Graph
Browse models, networks, integrators and studies as a linked graph. Open any entity to its ontology grounding, parameters and source.
Browse resources →Experiment Builder
Assemble a runnable experiment — network, dynamics, integrator, coupling, observation — and export it as validated YAML for the tvbo package.
REST API
Retrieve any model, network or experiment as schema-valid YAML from Python or any HTTP client — then load it back with from_string(...).run(). Built for humans and agents.
Store, share and reproduce your own experiments
Create an account to keep your models and experiments alongside the curated database — public by default, private with an API key, and loadable straight back into Python.
Create an account →