Abstract. We previously proposed a neural segmentation model suitable for implementation with complementary metal-oxide-semiconductor (CMOS) circuits. The model consists of neural oscillators mutually coupled through synaptic connections. The learning is governed by a symmetric spike-timing-dependent plasticity (STDP). Here we demonstrate and evaluate the circuit operation of the proposed model with a network consisting of six oscillators. Moreover, we explore the effects of mismatch in the threshold voltage of transistors, and demonstrate that the network was tolerant to mismatch (noise). Key words: Analog circuits, Neural networks, Spiking neurons, STDP, Neural segmentation, Noise tolerance