Lim, Jae Gwang Park, Sung-jae Lee, Sang Min Jeong, Yeonjoo Kim, Jaewook Lee, Suyoun Park, Jongkil Hwang, Gyu Weon Lee, Kyeong-Seok Park, Seongsik Jang, Hyun Jae Ju, Byeong-Kwon Park, Jong Keuk Kim, Inho 2024-08-29T05:30:22Z 2024-08-29T05:30:22Z 2024-08-29 2024-08 https://pubs.kist.re.kr/handle/201004/150512 Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up. English Nature Publishing Group Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model Article 10.1038/s41598-024-68359-x 1 Scientific Reports, v.14, no.1 Scientific Reports 14 1 Y scie scopus 001283438200092 2-s2.0-85200250559 Multidisciplinary Sciences Science & Technology - Other Topics Article PATTERN DESIGN