Wendling extended — noise σ=0.01 (strong physiological)

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 _jr_sigmoid(v, v_max=5.0, r=0.56, v0=6.0):
    """Jansen-Rit population sigmoid: S(v) = v_max / (1 + exp(r*(v0 - v)))."""
    return v_max * _stable_expit(r * (v - v0))

def rhs(t: float, y: np.ndarray) -> np.ndarray:
    y0v, y1v, y2v, y3v, y4v = y[0], y[1], y[2], y[3], y[4]
    y5v, y6v, y7v, y8v, y9v = y[5], y[6], y[7], y[8], y[9]

    S_pyr = _jr_sigmoid(y1v - y2v - y3v)
    S_exc = _jr_sigmoid(C1 * y0v)
    S_slow = _jr_sigmoid(C3 * y0v)
    S_fast = _jr_sigmoid(C5 * y0v - C6 * y4v)
    S_self = _jr_sigmoid(C3 * y0v)

    return np.array([
        y5v,                                                # ẏ₀
        y6v,                                                # ẏ₁
        y7v,                                                # ẏ₂
        y8v,                                                # ẏ₃
        y9v,                                                # ẏ₄
        A * a * S_pyr - 2.0 * a * y5v - a2 * y0v,          # ẏ₅
        A * a * (p + C2 * S_exc) - 2.0 * a * y6v - a2 * y1v,  # ẏ₆
        B * b * C4 * S_slow - 2.0 * b * y7v - b2 * y2v,    # ẏ₇
        G * g * C7 * S_fast - 2.0 * g * y8v - g2 * y3v,    # ẏ₈
        B * b * S_self - 2.0 * b * y9v - b2 * y4v,         # ẏ₉
    ])