the input of the simple spiking neuron (SSN) at time t is a vector u_{t−1} of the binary values representing the presence or absence of an input spike at the current time step.

v_{t} = ∑_{j} w_{j}u_{t−1}^{j} the SSN uses a stochastic threshold model, where the neuron potential affects only the probability of firing a

spike (i.e., taking the action at = 1) at time t:

P r(a_{t} = 1) = σ(v_{t}) = 1/(1 + exp(−v_{t})) The action at = 1 is represented by firing a spike at time t and, correspondingly, the absence of a spike represents

the action at = 0, which has probability 1 − s(v_{t}).

Figure 1: Number of trials needed to find out a hidden and unique PIN, averaged after 100 runs as a function of n. Left: A result by a random search. Right: An exhaustive search one by one.