Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

A flexible optoelectronic neural transistor (OENT) that consists of a one‐step spin‐coated tri‐blend film composed of 2,7‐dioctyl[1]benzothieno[3,2‐b][1]benzothiophene (C8‐BTBT), poly(3‐hexylthiophene‐2,5‐diyl) (P3HT), and poly(methyl methacrylate) (PMMA) is demonstrated. The C8‐BTBT and P3HT phases in the film partially segregate into distinct domains, which combine to provide broadband spectrum sensing, and instant electrical‐processing capabilities dominated by C8‐BTBT. The OENT is sensitive to solar radiation from the near‐ultraviolet (NUV) and to visible (Vis) radiation from blue to red. When exposed to NUV radiation, the OENT responds sensitively and retains the memory of the exposure for over 10³ s. The OENT provides a warning of excessive chronic exposure to harmful NUV. These properties allow high‐pass filtering with different cut‐off frequencies f_c that can restrict the reception of blue, green, or red. These switchable f_c enables sensitive image reconstruction and multitarget monitoring. The device combined with a chitosan gel achieves strictly defined short‐range plasticity of <1 s that can achieve diverse instant‐computing applications such as spatiotemporally correlated coding and logic functions. Stable real‐time signal processing facilitates the realization of a Morse‐code recognition system constructed using neuro‐morphological hardware, achieving highly accurate character recognition. This study provides a useful resource that can have applications in wearable biomedical electronics and multimodal neuromorphic computing.

Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, a core technology in smart agriculture, has revolutionized the lettuce industry through powerful computer vision techniques like convolutional neural networks (CNNs) and YOLO-based models. This review systematically examines deep learning applications in lettuce production, including pest and disease diagnosis, precision spraying, pesticide residue detection, crop condition monitoring, growth stage classification, yield prediction, weed management, and irrigation and fertilization management. Notwithstanding its significant contributions, several critical challenges persist, including constrained model generalizability in dynamic settings, exorbitant computational requirements, and the paucity of meticulously annotated datasets. Addressing these challenges is essential for improving the efficiency, adaptability, and sustainability of deep learning-driven solutions in lettuce production. By enhancing resource efficiency, reducing chemical inputs, and optimizing cultivation practices, deep learning contributes to the broader goal of sustainable agriculture. This review explores research progress, optimization strategies, and future directions to strengthen deep learning’s role in fostering intelligent and sustainable lettuce farming.

Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neural-imitating electronics. This segregation hinders coordination and functionality within the neuromorphic system. Here, we present a flexible device tailored for neuromorphic computation and muscle actuation. Each individual device component emulates essential synaptic functions for neural computing, while the collective ensemble replicates muscle actuation in response to efferent neuromuscular commands. These properties stem from densely-packed, hydrophilic nanometer-sized channels, and the erection of a high-entropy, intricately silver nanowires to capture and store of hydrated cations. Leveraging the remarkable deformation effect, we demonstrate hazard detection-avoidance robot, and multidimensional integration for arbitrary programmed shapes like 360° panoramic information capture and soft-bodied biological deformations wherein localized responses to stimuli are harmoniously integrated to achieve arbitrary coordinated motion. These results provide a significant avenue for the development of future flexible electronics and bio-inspired systems.