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Artificial synaptic devices and systems have become the hotspot due to the parallel computing, high plasticity, integration of storage and processing to meet challenges of the traditional Von Neumann computers....

The rapid development of digital technology results in a tremendous increase in computational tasks that impose stringent performance requirements on next‐generation computing. Biological neurons with fault tolerance and logic functions exhibit powerful computing capacity when facing complex real‐world problems, which strikes the inspiration for the development of highly energy‐efficient brain‐like computing. Herein, a novel device architecture, an electrolyte‐manipulated ferroelectric organic neuristor, which emulates biological neurons to perform both neuromorphic and reconfigurable logic‐in‐memory operations in a single cell, is proposed. The interfacial coupling of ions and dipoles in the neuristor contributes to the tunable synaptic behaviors of short‐ to long‐term plasticity. Notably, by virtue of lateral capacitive coupling, the neuristor is effectively controlled by multiple in‐plane gates to achieve heterosynaptic plasticity. An artificial neural network exhibits robust recognition ability with high accuracy of 93.7% in speech recognition, further demonstrating the feasibility of the neuristor for neuromorphic computing. Additionally, reconfigurable logic‐in‐memory operations (OR and AND) are successfully demonstrated in a single device. Therefore, the devices shed new light on the development of more brain‐inspired computing systems in the era of big data.

The human visual system (HVS) has the advantages of a low power consumption and high efficiency because of the synchronous perception and early preprocessing of external image information in the...