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Fluorescence super-resolution microscopy has, over the last two decades, been extensively developed to access deep-subwavelength nanoscales optically. Label-free super-resolution technologies however have only achieved a slight improvement compared to the diffraction limit. In this context, we demonstrate a label-free imaging method, i.e., hyperbolic material enhanced scattering (HMES) nanoscopy, which breaks the diffraction limit by tailoring the light-matter interaction between the specimens and a hyperbolic material substrate. By exciting the highly confined evanescent hyperbolic polariton modes with dark-field detection, HMES nanoscopy successfully shows a high-contrast scattering image with a spatial resolution around 80 nm. Considering the wavelength at 532 nm and detection optics with a 0.6 numerical aperture (NA) objective lens, this value represents a 5.5-fold resolution improvement beyond the diffraction limit. HMES provides capabilities for super-resolution imaging where fluorescence is not available or challenging to apply.

 

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.