Fig 1. A diagram illustrating the BSN.

Neurons receive stimulus input projected onto the transpose of a set of linear weights, W^⊤, and the output is reconstructed by filtering spikes through the same weights, W. Neurons are connected via two coupling weights: fast synapses, W^⊤W, which instantaneously propagate individual spikes through the network, and slow synapses, $W{}^{\top }(A+\frac{1}{\tau })W$, which implement network dynamics by feeding the filtered spike trains back into all neurons in the network. The network is divided into two equal populations of positive (red) and negative (blue) output weights, whose spikes have opposite effects on network output. Self-connections for these neurons are shaded in their respective colors, for visualization, but are always negative.