Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Triboelectric nanogenerator and artificial intelligence to promote precision medicine for cancer

Electronic nose and its application in the food industry: a review

Diagnostic and therapeutic illumination on internal organs and tissues with high controllability and adaptability in terms of spectrum, area, depth, and intensity remains a major challenge. Here, we present a flexible, biodegradable photonic device called iCarP with a micrometer scale air gap between a refractive polyester patch and the embedded removable tapered optical fiber. ICarP combines the advantages of light diffraction by the tapered optical fiber, dual refractions in the air gap, and reflection inside the patch to obtain a bulb-like illumination, guiding light towards target tissue. We show that iCarP achieves large area, high intensity, wide spectrum, continuous or pulsatile, deeply penetrating illumination without puncturing the target tissues and demonstrate that it supports phototherapies with different photosensitizers. We find that the photonic device is compatible with thoracoscopy-based minimally invasive implantation onto beating hearts. These initial results show that iCarP could be a safe, precise and widely applicable device suitable for internal organs and tissue illumination and associated diagnosis and therapy. 

/pdf/a-biodegradable-flexible-photonic-patch-for-in-vivo-drvzmwqv.pdf

A biodegradable, flexible photonic patch for in vivo phototherapy

<h2>Summary</h2> Creatures in nature adapt to changing environments by shaping their structures and functions in ingenious ways. A major focus of current biomedical research is seeking inspiration from the structure, function, and behavior of these natural organisms and combining biological principles with materials science and engineering to help solve great challenges in healthcare, medical devices, and biomedical technologies. Therefore, biomimetic design is leading the revolution in the biomedical field, facilitating innovation in diverse areas, including biomaterials, drug delivery, tissue engineering, biosensing, medical devices, and bionic prosthetics. These applications have vastly expanded the boundaries of biomedicine and have profound implications for improving human ability to diagnose and treat diseases, as well as elevate patients' quality of life. This review focuses on the biomimetic design strategies and applications of disease diagnosis, treatment, and rehabilitation in the biomedical field in recent years, aiming to provide new insights for bionic exploration. 

Biomimetic design strategies for biomedical applications

An Odor Recognition Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network for Spoiled Food Identification

Among basic taste sensations, bitter taste is vital to the survival of mammals due to its indispensable role in toxin prediction or identification, so the identification of bitter compounds is of great value in the pharmaceutical and food industry. Recently, bitter taste receptor (T2Rs)-based biosensors have been developed for specific bitter detection. However, the taste biosensors based on taste cells/tissues suffer from simple function, low sensitivity, low content, and limited parameters. Here, to establish a high-content, highly sensitive, and multifunctional taste biosensor, we developed a multifunctional hybrid integrated cardiomyocyte biosensor (HICB) for bitter detection. Due to the expression of bitter taste receptors in cardiomyocytes, the HICB can recognize the specific bitter agonists by synchronously recording the extracellular field potential (EFP) and mechanical beating (MB) signals from the cultured cardiomyocytes in vitro. Multiple feature parameters were defined and extracted from the electromechanical signals of cardiomyocytes to analyze the specific responses to four typical bitter compounds. The radar map, heat map, and principal component analysis (PCA) were used to visualize and classify the specific responses. Moreover, bitter-induced cardiotoxicity also was chronically evaluated, and these bitter compounds presented an inhibition effect on the electrophysiological and contractile activities of cardiomyocytes. This high-content HICB offers an alternative platform for both bitter detection and cardiotoxicity assessment, showing promising applications in the fields of taste detection and toxicity screening.

Hybrid Integrated Cardiomyocyte Biosensors for Bitter Detection and Cardiotoxicity Assessment.

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An Odor Recognition Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network for Spoiled Food Identification

Hybrid Integrated Cardiomyocyte Biosensors for Bitter Detection and Cardiotoxicity Assessment.