A Four‐Terminal TiO 2− x Memristor Architecture Enabling Multidimensional Associative Learning

Associative learning, a key higher‐order function of biological synapses, remains challenging to implement in hardware due to limitations in existing memristor architectures. Here we develop a four‐terminal TiO 2 −x memristor enabling two‐dimensional control of oxygen vacancy distributions, exhibiting stable non‐filamentary resistive switching accompanied by visible electrocoloring with applied voltages. Using a single four‐terminal device, we demonstrate bidirectional Pavlovian conditioning encompassing both learning and forgetting processes. Furthermore, by arranging multiple memristors based on this architecture, multidimensional associative learning of two‐dimensional image data is realized without involving any external circuits or computing units. These findings indicate that the proposed memristor architecture functions as a self‐contained physical learning element capable of acquiring and updating associations between stimuli. This work thus provides a conceptual framework for neuromorphic hardware that implements higher‐order associative functions, advancing beyond conventional software‐based artificial neural networks.