Module wilson_cowan 

Batch parity with WilsonCowanUnit.step in src/sc_neurocore/neurons/models/wilson_cowan.py (Wilson & Cowan 1972, Biophys. J. 12:1–24).

Per step: s_e = sigmoid(w_ee · E − w_ei · I + ext) s_i = sigmoid(w_ie · E − w_ii · I) E += (−E + s_e) · dt / τ_e I += (−I + s_i) · dt / τ_i

where sigmoid(x) = 1 / (1 + exp(−a·(x − θ))).

The model is deterministic (no noise), so bit-exact parity with the Python primary requires only matching arithmetic — no pre-drawn RNG buffer needed.

Functions

sigmoid 🔒

simulate

Simulate ext_input.len() Wilson-Cowan iterations, writing per-step E and I traces into caller-allocated buffers. Returns final (E, I) for convenience.