Spontaneous Threshold Lowering Neuron using Second‐Order Diffusive Memristor for Self‐Adaptive Spatial Attention

Abstract

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In-memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first-order dynamics. Here, a second-order memristor is experimentally demonstrated using yttria-stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second-order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL-based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second-order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high-efficiency, compact footprint, and hardware-encoded plasticity.

Memristor Based on Silk Fibroin and Graphene Oxide Blend for Electrical and Optical Synaptic Behavior

“…Organic materials have long been a significant focus of interest, particularly within the realm of resistive random-access memory (RRAM), where they demonstrate remarkable potential. − These materials offer inherent advantages, such as lightweight properties, flexibility, and cost-effectiveness in manufacturing. Additionally, RRAM devices composed of organic materials display varying resistance states, transitioning between high-resistance states (HRS) and low-resistance states (LRS) under voltage bias. − This configuration closely resembles the structure of biological neural synapses, making it exceptionally well suited for constructing neural computing structures.…”