Initial synaptic weight distribution for fast learning speed and high recognition rate in STDP-based spiking neural network

•We analyze that the initial synaptic weight distribution affects the performance, such as the learning speed, recognition rate and the power consumption in the spiking neural networks (SNNs) based on spike-timing-dependent plasticity (STDP) learning rule.•A thin-film transistor (TFT)-type NOR flash memory is used as a synaptic device.•In this fully connected two-layer neuromorphic system using the proposed pulse scheme, the results with and without the homeostasis functionality were analyzed separately.•In addition, power consumption of the network in various initial synaptic weight distributions, and recognition rate that varies with the number of output neurons are also investigated.•In pattern recognition for 28 × 28 MNIST handwritten patterns, higher performance is achieved in various aspects when the initial synaptic weights are distributed near the maximum value. We analyze that the initial synaptic weight distribution affects the performance, such as the learning speed, recognition rate and the power consumption in the spiking neural networks (SNNs) based on spike-timing-dependent plasticity (STDP) learning rule. A thin-film transistor (TFT)-type NOR flash memory is used as a synaptic device. In this fully connected two-layer neuromorphic system using the proposed pulse scheme, the results with and without the homeostasis functionality were analyzed separately. In addition, power consumption of the network in various initial synaptic weight distributions, and recognition rate that varies with the number of output neurons are also investigated. In pattern recognition for 28 × 28 MNIST handwritten patterns, higher performance is achieved in various aspects when the initial synaptic weights are distributed near the maximum value.