Neuro: Single Neuron
This scenario simulates a single model neuron being driven by a steady input. It’s a “hello world” for neural dynamics: input → membrane voltage → spikes.
What you’re simulating
- A neuron integrates input over time.
- When it crosses a threshold, it emits a spike (an action potential).
- Depending on the model settings, you can see regular spiking, adaptation, or silence.
Run it (web UI)
- Open Examples
- Choose Neuro → Single Neuron
- Click Run
Run it (local SimUI)
From the B‑Simulant library repo:
pip install "bsim[ui]"
python examples/neuro_simui_demo.py --mode single --port 8765Open http://localhost:8765/ui/.
What results to expect
- Raster / spikes: a simple spike train over time.
- Membrane voltage (Vm): repeating rises and sharp resets when a spike occurs.
Parameter presets (and what they mean)
| Goal | Steps | dt (s) | Expected result | What it means |
|---|---|---|---|---|
| Quick preview | 1,000 | 0.0001 | A few spikes (or none). | Confirms whether the neuron is in a “spiking” regime with the current settings. |
| Standard run | 3,000 | 0.0001 | Regular spiking with clear Vm resets. | A stable firing pattern under steady input. |
| Longer run | 20,000 | 0.0001 | Possible adaptation (spacing between spikes changes) depending on the neuron type. | Some neuron models change their response over time even with constant input. |
| Faster (less detail) | 3,000 | 0.0002 | Similar pattern, but spikes can look less sharp. | Larger dt runs faster but can distort fine timing/shape. |
What to try (and why)
- Increase Steps to see whether firing stays consistent or adapts.
- Adjust dt if the trace looks too jagged (smaller dt can be smoother but slower).
In the Biosimulant web UI, this example is the “baseline” you can compare against more complex network scenarios.