Figure 1.

Illustration waveforms of the spike-based perceptron learning rule showing key parameters from Equations (1–5). The Calcium variable plot shows the ranges defined by ${\theta }_{up}^l,{\theta }_{up}^h,{\theta }_{down}^l,{\theta }_{down}^h$ within which synaptic plasticity is active according to Equations (1) and (2). The post-synaptic neuron membrane potential plot shows the threshold θ_V. Incoming synapses can be depressed (potentiated) if V_mem(t) is below (above) θ_V. The bottom plot showing the synaptic state X(t) illustrates the jump and drift mechanism. On each pre-synaptic spike, the mutually exclusive conditions in Equations (1) and (2) are evaluated. If the condition in Equations (1) and (2) is fulfilled, the synaptic state jumps up (down) by a step a (b). The synaptic state is continuously drifting to a high or low state depending on whether it is above or below the threshold θ_X, respectively.