ing configuration ζ t0, composed by (**W,~b, ~σ**t0 )
3**, i.e. the initial condition of**
the dynamics. Neurons are synchronously updated by applying iteratively Eq. (1) for a sufficiently large time tmeas, the maximum **convergence time**
allowed4. If W **is asymmetric and according to its asymmetry degree, a**
periodical orbit of length l is reached after a transient τ **, giving the asymptotic configuration** ζ tmeas , composed by (W,~b, ~σtmeas **). In the following, all**
the procedures will make use of this concept of asymptotic configuration and its eventual distance from the correct behavior. This is **motivated from** the assumption that only stable asymptotical configurations can account for stability and invariance of response typical of human cognitive processes. In principle, tc **can be viewed as the time needed by the cognitive elaboration.**
Training phase: a problem of global optimization**. We choose a**
subset of n **Boolean functions as possible instances of the training problem.**
For each γ**-th function we select** N
γ I
input neurons from which the remaining ones take entries for the update dynamics and just one, that is N
γ O **= 1,**
output neuron from which, after the attainment of a ζ tmeas **, we will measure**
the computed output.

Moreover, for each γ**-th function we generate the corresponding training**
set ε γ, for which all possible examples are given by coupling one of the inputs ξ γ,inp µ **with the respective wanted output** ξ γ,out µ , for µ = 1**, ..., P**γ.

The presentation of the entire training set ε **is consequently just one training**
epoch5**. At the end of each epoch, the error signal** E(ζ tmeas **) will be:**