No clear winner. The survival gap is under 10 percentage points and the balanced-score gap is under 0.05, so neither SolvSRK nor the best baseline clears the win threshold. Either works — choose on cost, licensing, or integration effort. All verdicts →
2D Wilson-Cowan E/I model with separate population sigmoids. Excitatory and inhibitory firing rate dynamics with τ_E=2.5 ms, τ_I=3.75 ms. Canonical minimal neural mass model.
Wilson & Cowan (1972), Biophys. J.
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
def _stable_expit(x):
"""Numerically stable sigmoid 1/(1+exp(-x)), scalar or array."""
x = np.asarray(x, dtype=float)
return np.where(
x >= 0,
1.0 / (1.0 + np.exp(-x)),
np.exp(x) / (1.0 + np.exp(x)),
)
def rhs(t: float, y: np.ndarray) -> np.ndarray:
r_E, r_I = y[0], y[1]
input_E = c_EE * G_E * r_E - c_EI * r_I + I_ext * G_E
input_I = c_IE * r_E
F_E = float(_stable_expit(sigma_E * (input_E - mu_E)))
F_I = float(_stable_expit(sigma_I * (input_I - mu_I)))
dr_E = (-r_E + F_E) / tau_E
dr_I = (-r_I + F_I) / tau_I
return np.array([dr_E, dr_I])Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: low
Default noise: none
Recommendation snapshot
Clean best: SciPy LSODA
Noisy best: SciPy BDF
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Wilson-Cowan excitatory-inhibitory (wilson-cowan-excitatory-inhibitory)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SciPy LSODASciPy | 100% | 16.0 | 316 | 4 ms | 1.000 |
| 2 | CVODE Adamsexternal | 100% | 16.0 | 239 | 10 ms | 1.000 |
| 3 | SolvSRK | 100% | 16.0 | 287 | 5 ms | 1.000 |
| 4 | SciPy RadauSciPy | 100% | 15.5 | 1,207 | 32 ms | 0.988 |
| 5 | SciPy BDFSciPy | 100% | 14.1 | 473 | 19 ms | 0.955 |
| 6 | TRBDF2external | 100% | 13.9 | 727 | 4.70 s | 0.950 |
| 7 | FBDFexternal | 100% | 12.5 | 553 | 4.61 s | 0.917 |
| 8 | CVODE BDFexternal | 100% | 11.2 | 308 | 11 ms | 0.886 |
| 9 | Vern9external | 100% | 10.8 | 3,298 | 3.67 s | 0.876 |
| 10 | Vern7external | 100% | 9.9 | 2,162 | 3.66 s | 0.855 |
| 11 | SciPy DOP853SciPy | 100% | 9.0 | 1,790 | 23 ms | 0.832 |
| 12 | Tsit5external | 100% | 8.0 | 2,070 | 863 ms | 0.809 |
| 13 | SciPy RK45SciPy | 100% | 7.9 | 1,982 | 27 ms | 0.808 |
| 14 | SciPy RK23SciPy | 100% | 7.6 | 2,255 | 34 ms | 0.801 |
At Clean, best balanced arm is SciPy LSODA · SolvSRK survival 100%, SCD 16.0.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
SolvScout · free
This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.
SolvSRK · 30-day trial
SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_wilson_cowan_excitatory_inhibitory_2026,
title = {Resonix Evidence Portal: Wilson-Cowan excitatory-inhibitory},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/wilson-cowan-excitatory-inhibitory}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status