Implementation of BCM Learning Rule Based on Room Temperature Derived -IGZO Synaptic Transistors

In recent years, the implementation of neuromorphic computation on devices has attracted much attention. Bienenstock-Cooper-Munro (BCM) learning rule is considered as the most important synaptic model in biology and is more in line with the working principle of neuromorphic computational systems; therefore, it is of great significance to simulate BCM behavior on solid-state neuromorphic devices. In this work, we present an electric-double-layer (EDL) transistor based on alpha-IGZO with sodium alginate (SA) electrolyte film as the gate dielectric. The integrated transistor devices at room temperature have demonstrated good performance, including low subthresh-old swing (SS) of 150 mV/decade, switching ratio greater than 106, and threshold voltage about 1.6 V. Some basic neuromorphic synaptic functions were also simulated, including excitatory postsynaptic current (EPSC), spike duration-dependent EPSC behavior, paired pulse facilita-tion (PPF), spike number-dependent EPSC behavior and the high-pass filtering. More importantly, the BCM learning rule containing historical frequency-dependent plasticity and adjustable frequency threshold is implemented on this device. This study contributes to the effective reduction of energy loss and protection of neural circuits from stim-ulus toxicity, which further provides an effective method to improve the efficiency of neural networks in artificial intelligence systems.