*Article* **Solving Overlapping Pattern Issues in On-Chip Learning of Bio-Inspired Neuromorphic System with Synaptic Transistors**

**Hyungjin Kim 1,\* and Byung-Gook Park 2,\***


Received: 23 November 2019; Accepted: 18 December 2019; Published: 21 December 2019

**Abstract:** Recently, bio-inspired neuromorphic systems have been attracting widespread interest thanks to their energy-efficiency compared to conventional von Neumann architecture computing systems. Previously, we reported a silicon synaptic transistor with an asymmetric dual-gate structure for the direct connection between synaptic devices and neuron circuits. In this study, we study a hardware-based spiking neural network for pattern recognition using a binary modified National Institute of Standards and Technology (MNIST) dataset with a device model. A total of three systems were compared with regard to learning methods, and it was confirmed that the feature extraction of each pattern is the most crucial factor to avoiding overlapping pattern issues and obtaining a high pattern classification ability.

**Keywords:** neuromorphic system; on-chip learning; overlapping pattern issue; pattern recognition; synaptic device; spiking neural network
