Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing

Abstract

The human brain's efficiency and adaptability in processing information is largely attributed to spatiotemporal spiking activities and intrinsic plasticity—the ability of neurons to autonomously modulate their excitability. Mott memristors, with their threshold switching characteristics, have been effectively utilized as artificial neurons, or neuristors, to generate spiking activities. However, the implementation of intrinsic plasticity and its significance in neuromorphic computing has yet to be systematically explored. Here, a frequency switching (FS) neuristor is presented that emulates neuron's intrinsic plasticity characteristics. By combining a volatile Mott memristor with a non-volatile valence change memory (VCM) memristor, the FS neuristor achieves programmable multi-level frequency–voltage (f–V) characteristics analogous to the transfer functions of neuronal intrinsic plasticity. Through device-based simulations of sparse neural networks, it is proposed that this intrinsic plasticity acts as memory and processor itself, enhancing network performance and reducing energy consumption. Additionally, intrinsic plasticity endows the network with structural plasticity, enabling full recovery of the network's performance after random neuron damage, suggesting a pathway toward more adaptive and resilient neuromorphic computing systems.