  We present an on-chip trainable neuron circuit. Our proposed circuit suits
bio-inspired spike-based time-dependent data computation for training spiking
neural networks (SNN). The thresholds of neurons can be increased or decreased
depending on the desired application-specific spike generation rate. This
mechanism provides us with a flexible design and scalable circuit structure. We
demonstrate the trainable neuron structure under different operating scenarios.
The circuits are designed and optimized for the MIT LL SFQ5ee fabrication
process. Margin values for all parameters are above 25\% with a 3GHz throughput
for a 16-input neuron.
