Ah, it's spiking edges as opposed to spiking node activations. I like this because it applies to 1 of many connections uniquely, rather than dividing the entire

Ah, it's spiking edges as opposed to spiking node activations. I like this because it applies to 1 of many connections uniquely, rather than dividing the entire graph by spiking neurons. Exponentially more edges than nodes in a dense net. Connections are always more important than entities. ```The dendrites generated local spikes, had their own nonlinear input-output curves and had their own activation thresholds, distinct from those of the neuron as a whole``` How would this work in practice? Apply activations to multiplication values of the weight, or just don't perform the multiplication if the activation of the node is low?