Single-neuron Multimodal

Build, simulate, and inspect a single Izhikevich neuron with all eight proxy readouts. Extract spikes, voltage, field readouts, and reproducible output bundles.

Open as Colab notebook

Recommended: Open the full interactive tutorial in Colab:

Or download and run locally: .legacy/notebooks/01_single_neuron_multimodal.ipynb

Setup

import jaxfne as jtfne
import matplotlib.pyplot as plt

Configure the neuron

cfg = (
    jtfne.configuration()
    .network(n=1)
    .emitter(family="izhikevich", preset="regular_spiking")
    .field(domain="point")
    .probe(name="single_neuron", modes=["spikes", "V_m"])
)

Key parameters:

• n=1: One neuron
• preset="regular_spiking": Izhikevich parameter set (RS, FS, IB, etc. available)
• domain="point": No spatial extent (suitable for single neuron)

Build the model

model = jtfne.construct(cfg)

Simulate

sim = jtfne.simulation(
    duration_ms=100.0,
    dt_ms=0.1,
    seed=0
)

signals = model.simulate(sim)

signals contains:

• signals.spikes: [T, N] spike matrix (binary)
• signals.V_m: [T, N] voltage trace (mV)

Compute readouts

readouts = model.compute_readout(signals, [
    jtfne.readout_spec("spike_count", "spike_count"),
    jtfne.readout_spec("mean_voltage", "voltage_mean"),
])

for r in readouts:
    print(f"{r.name}: {r.value:.2f} [{r.status}]")

Visualize

fig, axes = plt.subplots(2, 1, figsize=(10, 6))

# Voltage trace
axes[0].plot(signals.V_m[:, 0])
axes[0].set_ylabel("Voltage (mV)")
axes[0].set_title("Neuron membrane potential")

# Spike raster
spike_times = signals.spikes[:, 0].nonzero()[0]
axes[1].vlines(spike_times, 0, 1)
axes[1].set_ylabel("Spike")
axes[1].set_xlabel("Time (ms)")
axes[1].set_xlim(0, 100)

plt.tight_layout()
plt.show()

Key takeaways

• Single neuron simulations are the foundation
• Output is JSON-serializable (see model.manifest(signals, readouts))
• Next: move to Two-neuron E/I for dynamics

Further reading

• Probe operators
• Output bundles