Nociceptor-Enhanced Spike-Timing-Dependent Plasticity in Memristor with Coexistence of Filamentary and Non-Filamentary Switching

In the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high-energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low-power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury-enhanced spike-time-dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles. A bilayer-structured memristor has been developed, showcasing reliable resistive switching in both filamentary and non-filamentary modes. This memristor displays diverse capabilities, serving as a unified entity capable of reservoir computing, emulating artificial nociceptors, and functioning as a synapse. Through the application of Hebbian learning rules, it facilitates the comprehension of how external pain influences variations in brain activity. image