A Photonic Neuromorphic Autonomous Perceptual Decision System Enabled by MOF‐Based Array
Source abstract
Abstract
Abstract Emulating human‐like perceptual decisions remains a critical challenge in humanoid intelligent robots, as existing neuromorphic devices lack multi‐signal parallel processing for advanced cognitive functions such as autonomous situation adaptation and dynamic environment interaction. Here, a neuromorphic autonomous perceptual decision system using an optoelectronic synapse array based on a heterostructure of 1D_ZnO nanorod (NR) and 0D_Cu 3 (HHTP) 2 MOFs is reported. A significant improvement in responsivity (≈6.77 mA W −1 ) and detectivity (≈1.08 × 10 10 Jones) over pristine components is achieved in this 1‐0D heterostructure at 405 nm. Benefiting from the synaptic characteristics of the hybrid architecture, the bio‐visual selective memorizing and forgetting behavior are successfully demonstrated in an array. Moreover, a photonic neuromorphic autonomous perceptual decisions system composed of “visual evidence accumulation‐action preparation‐decision‐action” is developed, enabling an autonomous turn of a robotic dog through multi‐signal processing. This work bridges neuromorphic materials with bioinspired cognitive architectures, offering a scalable framework for autonomous systems that replicate higher‐order brain functions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu 611731 P. R. China
Shimmer Center Tianfu Jiangxi Laboratory Chengdu 641419 P. R. China
School of Future Technology University of Electronic Science and Technology of China Chengdu 611731 P. R. China
Funding
National Key Research and Development Program of China · Science and Technology Department of Sichuan Province
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