Unsupervised online learning of temporal information in spiking neural network using thin-film transistor-type NOR flash memory devices.

Brain-inspired analog neuromorphic systems based on the synaptic arrays have attracted large attention due to low-power computing. Spike-timing-dependent plasticity (STDP) algorithm is considered as one of the appropriate neuro-inspired techniques to be applied for on-chip learning. The aim of this study is to investigate the methodology of unsupervised STDP based learning in temporal encoding systems. The system-level simulation was performed based on the measurement results of thin-film transistor-type asymmetric floating-gate NOR flash memory. With proposed learning methods, 91.53% of recognition accuracy is obtained in inferencing MNIST standard dataset with 200 output neurons. Moreover, temporal encoding rules showed that the number of input pulses and the computing power can be compressed without significant loss of recognition accuracy compared to the conventional rate encoding scheme. In addition, temporal computing in a multi-layer network is suitable for learning data sequences, suggesting the possibility of applying to real-world tasks such as classifying direction of moving objects.