Optimal Learning Rules for Discrete Synapses
Figure 1
Binary input vectors xa are presented, with each component having probability p of being in the high state. Synaptic weights wa occupy one of W discrete states, whose values are equidistantly spaced around zero. The output h is the inner product of the vector of inputs with the weight vector.