Constructing a Complex Hybrid Neural Network for Biomimetic Spatial and Temporal Perception
Artificial neural networks based on artificial synaptic thin-film transistors (ASTFTs) offer promising opportunities for efficient and scalable information processing. However, implementing both spatial and temporal processing within a unified system requires complex hybrid network architectures, placing high demands on the tunability of individual synaptic devices. Here, we report a reconfigurable photosensitive synaptic transistor composed of FAPbI₃ colloidal quantum dots (CQDs) and an InO x channel, integrated with a modulation scheme tailored for hybrid convolutional-sequence neural computing. The device enables mode-specific plasticity through programmable optical and electrical stimuli. For spatial signal processing, it achieves a long-term potentiation (LTP) linearity of 0.062, long-term depression (LTD) linearity of 0.143, and an asymmetry ratio (AR) of 0.185. For temporal learning, the dynamic fading memory (FM) time constant is tunable from 42 to 75 ms, enabling dynamic short-term memory (STM) behavior. Utilizing these synaptic features, we construct a hybrid CNN-GRU neuromorphic system that achieves 94.2% accuracy in real-time gesture recognition within only 7 training epochs, using a custom dataset over 200 times larger than conventional benchmarks.This work demonstrates a unified neuromorphic hardware platform capable of spatiotemporal cognition, offering new strategies for the development of intelligent optoelectronic systems.
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