  We present a two-layer fully connected neuromorphic system based on a
thin-film transistor (TFT)-type NOR flash memory array with multiple
postsynaptic (POST) neurons. Unsupervised online learning by
spike-timing-dependent plasticity (STDP) on the binary MNIST handwritten
datasets is implemented, and its recognition result is determined by measuring
firing rate of POST neurons. Using a proposed learning scheme, we investigate
the impact of the number of POST neurons in terms of recognition rate. In this
neuromorphic system, lateral inhibition function and homeostatic property are
exploited for competitive learning of multiple POST neurons. The simulation
results demonstrate unsupervised online learning of the full black-and-white
MNIST handwritten digits by STDP, which indicates the performance of pattern
recognition and classification without preprocessing of input patterns.
