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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.
 
$v_t = \sum_j w_j u^j_{t-1}$
 
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:
 
$Pr (a_t = 1) = \sigma(v_t) = 1/(1+exp(-v_t))$
 
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 
$1 - s (v_t).$




\begin{figure}[bhtp]
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\includegraphics[width=9cm,height=6cm]{spike-0.eps}
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\caption{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.}
\end{figure}


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