jaxfne TFNE-Izhikevich Single Emitter Explorer¶
Interactive browser-side preview for exploring reduced-emitter waveform dynamics before configuring a jaxfne scaffold.
Run status¶
artifact_class: interactive_tool
run_status: browser_euler_preview
model_status: parameter_intuition_only
field_solver_status: not_applicable
amplitude_status: native_unscaled
solver: browser_euler_dt_0.25ms
This tool runs entirely in the browser. It is a parameter intuition aid — not a jaxfne execution. All outputs are browser-computed previews for qualitative exploration only.
What this tool does¶
The explorer lets you sweep Izhikevich reduced-emitter parameters in real time and observe the resulting waveform, phase portrait, and nullcline structure. Eleven biological presets are included: E-Regular, E-Bursting, E-Chattering, E-Wide, PV, SST, VIP, FS, LTS, RZ (Resonator), and TC (Thalamocortical).
Use it to:
- Develop intuition for how
a,b,c,dshape spike patterns before a jaxfne run. - Compare E vs PV vs SST vs VIP waveform classes side by side.
- Understand the phase portrait (v vs u nullclines) for each cell type.
- Choose a starting drive level
Ibefore settingdrive_gainin AGSDR tuning.
Transition to jaxfne¶
After exploring parameters here, use the package-native path for any actual simulation:
import jaxfne as jtfne
# Configure with the presets that match your exploration
cfg = jtfne.suite2_v1_v4_config(seed=42, n_per_area=80, duration_ms=1000.0, dt_ms=0.1)
model = jtfne.construct(cfg)
bundle = jtfne.suite2_run_bundle(model, seed=42, duration_ms=1000.0, dt_ms=0.1)
The emitter presets in the explorer correspond to jtfne.suite2_celltype_presets(). The drive level I maps to the drive_gain tunable parameter in jtfne.agsdr().
Scope¶
- Browser Euler solver at dt=0.25 ms. Not the jaxfne JAX kernel.
- Outputs are waveform previews only. No source projection, no field readout.
amplitude_status: native_unscaled— arbitrary units throughout.- No biological mechanism is implied by the parameter sweep.
Interactive panel¶
Browser-only preview
The panel below runs a forward-Euler Izhikevich solver in JavaScript. It requires no server, no Python, and no JAX. Adjust sliders to explore — then use jaxfne for any run that produces evidence.
run_status: browser_euler_preview
model_status: parameter_intuition_only
amplitude_status: native_unscaled