Present vision restoration technologies have substantial constraints that limit their application in the clinical setting. In this work, we fabricated a subretinal nanoprosthesis using tellurium nanowire networks (TeNWNs) that converts light of both the visible and near-infrared–II spectra into electrical signals. The broad-spectrum coverage is made possible by a combination of narrow bandgaps, strong absorption, and engineered asymmetries. Implanted into blind mice, the TeNWNs restored pupillary reflexes and enabled visually cued learning under visible and near-infrared 1550-nanometer light. In nonhuman primates, TeNWNs elicited robust retina-derived neural responses, confirming biocompatibility and feasibility. By restoring lost photosensitivity and extending vision to near-infrared, this nanoprosthesis offers a promising approach for restoring vision.

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial–temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial–temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.

The neuronal circuits regulating sleep and those controlling autonomic and somatic motor functions suggest that a primary function of sleep is to suppress motor activity. Interestingly, the muscle twitches including eye movements (EMs) that occur during rapid eye movement sleep (REMs) represent a retained form of motor activity. Here, we identify a subset of cholinergic neurons within the oculomotor nucleus (nIII ^ChAT ) whose activity progressively increases prior to REM termination. Distinct from the extraocular muscles (EOMs)-projecting nIII ^ChAT neurons, we identified a subpopulation of nIII ^ChAT neurons projecting to the vlPAG. Optogenetic activation of these vlPAG-projecting nIII ^ChAT neurons robustly suppressed REMs without affecting EMs. Conversely, selective activation of EOMs-projecting nIII ^ChAT neurons reliably induced EMs but have no effect on the sleep-wake states. Clustering analysis of endoscopic imaging data revealed that these two identified subgroups of nIII ^ChAT neurons are organized into partially overlapping but functionally distinct networks. Viral tracing and in vivo electrophysiological recordings further demonstrate that these motor and sleep-modulating functions of nIII ^ChAT neurons are coordinated by genetically distinct populations of upstream nucleus papilio (NP) neurons. Our findings revealed the microcircuitry responsible for neural mechanism of REMs exit, where motor related function of the nIII is preparing the mammal to enter the subsequent wakefulness with elevated motor capabilities.

Background:The dorsal lateral geniculate nucleus (dLGN) exhibits remarkable functional diversity, yet a systematic classification of its neuronal properties and the subcortical mechanisms that modulate these representations remains to be fully elucidated.Methods: In this study, we performed a comprehensive functional mapping of the mouse dLGN using a diverse battery of visual stimuli. Neurons were grouped into clusters using unsupervised clustering of multi-dimensional response features, and the superior colliculus (SC) was pharmacologically silenced to assess its modulatory role.Results: We identified at least 33 distinct functional clusters characterized by specialized tuning for contrast, frequency, and speed, as well as direction and orientation selectivity, providing a high-dimensional atlas of dLGN diversity. SC suppression triggered a multidimensional reorganization of dLGN properties that was cluster-specific and selectively affected various functional indices, including contrast sensitivity and feature selectivity, depending on the cell group.Conclusions: These results indicate that distinct functional clusters of dLGN neurons exhibit heterogeneous modulation by SC inputs, demonstrating that collicular inputs are essential for shaping the diverse functional profiles of thalamic visual channels.