hnn-core
hnn-core: A specialized toolkit for modeling neocortical circuits and their measurable signals. Ranked #51 of 69 in Simulation Software by our editors (4.0/10); pricing: Open source; best for neuroscience researchers simulating cortical circuits.
At a glance
- Editor score4.0 / 10
- PricingOpen source
- Best forNeuroscience researchers simulating cortical circuits
- Paid fromFree
- Cloud deploymentYes
- Facts checked24 Sep 2026
Where it wins
- Models EEG/MEG dipoles, ERPs, rhythms, LFP, CSD and spiking activity
- Fits and optimizes model parameters against experimental dipole data
- Provides Python API, web-based GUI and MPI or Joblib parallel backends
Where it doesn't
- Its focus is limited to computational and experimental neuroscience
- Requires working with Python and the NEURON simulation environment
- Support is through community resources and documentation
Our verdict on hnn-core
For researchers studying how neocortical circuits generate measurable brain signals, hnn-core offers a Python API and web-based GUI for biophysically detailed simulation. It models cellular and circuit mechanisms behind EEG and MEG signals, including current dipoles, event-related potentials, and alpha, beta, and gamma rhythms. The package is aimed at computational and experimental neuroscience workflows, rather than general-purpose simulation needs.
Its distinctive strength is the range of outputs and the connection between circuit mechanisms and experimental data. Researchers can examine local-field potentials, current-source density, voltage traces, and spike activity; edit morphology, biophysical properties, and connectivity; and fit or optimize parameters against experimental dipole data. MPI and Joblib backends support parallel simulation. NEURON and MNE-Python integrations place it within established neuroscience workflows, while the GUI is built on Jupyter widgets and Voilà.
hnn-core is open-source Python software, not a commercial subscription, so pricing is not organized into paid tiers. Tutorials, examples, and documented installation workflows provide learning resources, with community and documentation as support channels. That makes it a fit for researchers and teams comfortable with a technical, code-centered workflow who need detailed cortical circuit modeling. It is a poor match for buyers seeking a broad simulation suite, a general-purpose visual modeling tool, or vendor-led support; its value depends on the problem being specifically about neuroscience circuits and their EEG/MEG signatures.
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hnn-core pricing
hnn-core fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| Discrete-event models | Not verified |
| System-dynamics models | Not verified |
| Agent-based models | Not verified |
| Finite-element analysis | Not verified |
| Multiphysics simulation | Not verified |
| Cloud deployment | Yes |
| API access | Yes |
| Deployment | Cloud, Self-hosted |
| Platforms | Windows, macOS, Linux |
| Support | Community, Docs |
| Built for | Solo, Small business, Mid-market (editorial estimate) |
| Integrations | 2 integrations: NEURON, MNE-Python |
| Pricing | Open source |
| Website | jonescompneurolab.github.io |
| Facts checked | 24 Sep 2026 |
hnn-core integrations
hnn-core lists 2 integrations on its own site.
- NEURON
- MNE-Python
Alternatives to hnn-core
- SimulinkA broad engineering simulation environment for modeling, verification, and code generation.9.4
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- Ansys FluentA broad CFD toolkit for engineering teams that need advanced physics and scalable solving.9.2
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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