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Computational imaging, as a novel technology utilizing encoded image acquisition, relies on intelligent decoding methods for effective image restoration and sensing. Optical computing‐based decoders can efficiently process and extract features from pre‐sensor information, reducing the computational burden on digital computers. However, mainstream parallel optical neural network (ONN) architectures based on wavefront propagation typically possess complex network structures and high‐precision parameters, which pose challenges in terms of precise fabrication and system calibration, as well as sensitivity to signal‐to‐noise ratios. In this work, a binary‐weighted optical computing engine is proposed with spatial multiplexing and aggregation (B‐OSMA), a large‐scale passive ONN implementation that achieves high‐efficiency image sensing. Employing B‐OSMA as an optical decoder, demonstrated image categorizing from 2% compressive is experimented sampling with 92.0% and 83.8% accuracy on MNIST and fashion‐MNIST datasets, respectively, approaching the performance of full‐precision electronic computing while reducing storage requirements by 97%. Compared to conventional ONNs with analog weights, the B‐OSMA exhibits enhanced resilience against systematic errors and ambient noise. This work represents a significant advancement towards practical applications of optical computing in image sensing. [ABSTRACT FROM AUTHOR]

Cerebellum possesses very rich motor control and learning capability which is critical for animals. In this study, we proposed a spiking neural network model of cerebellum for gain and phase adaptation in vestibulo-ocular reflex (VOR). VOR is a critical adaptive reflexive eye movement for maintaining a stable visual field. In this model (with neuron number at the order of 104), synaptic plasticity at parallel fiber-Purkinje cell synapses was considered. In particular, we have shown that the inhibitory inputs from molecular layer interneurons on Purkinje cells play a critical role in phase adaptation of VOR. The inhibitory input from interneurons indirectly affects the strength of long-term potentiation (LTP) and long-term depression (LTD), resulting in more drastic phase shift upon learning and hence allowing phase reversal of VOR. The strength of inhibitory input also affects the maximum phase shift that can be achieved. Our result is consistent with experiments in mutant mice with blocked inhibitory inputs.