Analog-circuit implementation of multiplicative spike-timing-dependent plasticity with linear decay

Neuromorphic engineering is a promising computing paradigm in next-generation information and communication technology. In particular, spiking neural networks are expected to reduce power consumption drastically owing their event-driven operation. The spike-timing-dependent plasticity (STDP) rule, which learns from local spike-timing differences between neurons, biologically plausible learning rule for (SNNs). this study, we designed simulated an analog circuit that reproduces the multiplicative STDP more flexible adaptive external signals. We also derived analytical expressions behavior of proposed circuit. These results provide important insights designing energy efficient neuromorphic devices applications including edge computing.