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name: brian2 description: "Use Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Brian2

Brian2 is a Python simulator for spiking neural networks. Use this skill when a request names Brian2, NeuronGroup, Synapses, Network, monitors, Brian units/equations, set_device, cpp_standalone, or a related simulation error. This skill is a self-contained operating guide for Brian2 2.9.0-style APIs; it does not require reopening the source repository.

Start safely

1. Establish an isolated Python environment with Python >=3.12.
2. Install the public distribution with python -m pip install brian2 (or use the documented Conda package). Install optional packages only for a selected workflow: a C++ compiler for Cython/standalone, GSL for GSL state updaters, and plotting/scientific packages only when needed.
3. Verify the installation from a neutral working directory: python -c "import brian2; print(brian2.__version__)".
4. For a read-only environment report, run scripts/check_brian2_env.py. It reports package/import/compiler/optional-dependency signals without changing files.
5. Begin with prefs.codegen.target = "numpy" and a tiny model when compiler availability is unknown. Move to Cython or C++ standalone only after reading the code-generation route and its limitations.

Choose the route

• Neuron equations, thresholds, resets, refractory behavior, subgroups, or custom events: read modeling.
• Units, equation-string grammar, namespaces, stochastic terms, or state updaters: read units-and-equations.
• Synapses, connectivity, delays, STDP, Poisson input, replay, or TimedArray: read synapses-and-inputs.
• run, Network, clocks, scheduling, snapshots, repeated trials, or progress/profile diagnostics: read simulation-and-recording.
• Spike/state/rate/event monitors, subset recording, export, or memory control: read recording.
• Cython/NumPy targets, set_device, C++ standalone, compiler/cache issues, or GSL: read code-generation.
• Morphology, compartments, SpatialNeuron, or SWC/section geometry: read spatial-models.
• Installation, preferences, cache, logging, missing dependencies, or a cross-cutting failure: read configuration-and-troubleshooting.

Most real tasks cross routes: define and validate equations first, create synapses and inputs second, assemble an explicit Network for nontrivial lifecycle control, add monitors before running, and choose a device only after the CPU/NumPy behavior is understood. Follow the links above rather than copying every API table into this router.

Core invariants

• A model equation is a Brian expression string, not arbitrary Python. Use Brian's bare supported functions (sin, exp, clip, rand, etc.) and declare dimensions explicitly.
• Synapses(...) defines pathways but does not create connections; call connect(...) before assigning synaptic state.
• A StateMonitor records at its configured schedule (default when="start"), and its array index is relative to the selected recording indices.
• Magic run collects visible objects only. Prefer Network(...) when objects are in containers, runs are staged, or train/test snapshots must be exact.
• A CPU/NumPy smoke does not prove Cython or C++ standalone. Those require a compiler-aware check and, for a standalone claim, an actual temporary build.
• Keep optional GSL, plotting, notebook, SciPy, Pandas, and multiprocessing capabilities explicit; absence of an optional package is not a core Brian2 import failure.

Shared references

• Read references/repo-provenance.md before deciding whether this graph matches a checkout or needs refreshing.
• Read references/troubleshooting.md for cross-cutting installation, import, dependency, cache, and lifecycle triage.
• Use the bundled smoke scripts in the owning sub-skill only after checking their --help output and selecting a tiny, bounded fixture.