Spike-timing-dependent plasticity model for low-frequency pulse waveform

The authors are studying pulse-type hardware neural networks (P-HNN) for generating the driving waveform of the robot. The P-HNN generates a driving waveform by the oscillatory pulse waveform. Oscillating frequency depends on the time constant of the oscillator. In the case of implementing into the integrated circuit (IC), a large time constant cannot construct to the limited space. This paper is discussing the generation of low-frequency pulse waveform without using a large time constant. Two cell body models pre-synaptic neuron (Pre) and post-synaptic neuron (Post) generate the high-frequency pulse (637 kHz). A slight time difference of both pulse waveform converts to the small voltage difference of spike-timing-dependent plasticity (STDP) model. The small voltage difference indicates the coupling coefficient of Pre and Post. The value of the coupling coefficient changing slowly, thus, the output neuron (Out) generates the burst-like waveform which can use as a low-frequency pulse waveform (37 Hz).