We consider a neural network that evolves in discrete time. At each timestep t, a neuron i either fires (fi(t) = 1) with probability ƒÐi(t), or does not fire (fi(t) = 0) with probability 1 . ƒÐi(t). We consider a neural network that evolves in discrete time. At each timestep t, a neuron i either fires (fi(t) = 1) with probability ƒÐi(t), or does not fire (fi(t) = 0) with probability 1 . ƒÐi(t). The efficacies wij can be
either positive or negative (corresponding to excitatory and
inhibitory synapses, respectively). A global reward signal
r(t) is broadcast to all synapses.