Algorithm     Input:     Target 1D map P (Lorenz peaks map), candidate sizes N ∈ {N_min, …, N_max}, tolerance ε_dyn for dynamical similarity.     For each N in {N_min, …, N_max} do     1. Construct an N–neuron MRNN with fixed architecture and     parameters constrained by (stability and boundedness).     2. Simulate the MRNN for the same range of control parameters as P     (e.g., ρ for Lorenz and η for the MRNN).     3. Compute dynamical similarity metrics:        (a) bifurcation pattern (number and order of period–doubling           windows),        (b) maximal Lyapunov exponent λ_max,        (c) qualitative shape of invariant density.     4. Measure the mismatch Δ(N) between the MRNN and the Lorenz map     in terms of the above metrics. End For     Select N* = argmin_N Δ(N) subject to:          (i) Δ(N*) ≤ ε_dyn (sufficient dynamical accuracy),         (ii) the MRNN has a bounded attractor,      (iii) sensitivity analysis remains interpretable (no excessive          parameter redundancy).     Output: Selected network size N* (in our case N* = 64).