A close view of the paraventricular thalamus: The paraventricular thalamus is a relay station connecting brainstem and hypothalamic signals that represent internal states with the limbic forebrain that performs associative functions in emotional contexts. Zhu et al. found that paraventricular thalamic neurons represent multiple salient features of sensory stimuli, including reward, aversiveness, novelty, and surprise. The nucleus thus provides context-dependent salience encoding. The thalamus gates sensory information and contributes to the sleep-wake cycle through its interactions with the cerebral cortex. Ren et al. recorded from neurons in the paraventricular thalamus and observed that both population and single-neuron activity were tightly coupled with wakefulness. Science , this issue p. 423 , p. 429

Neurostimulant drugs or magnetic/electrical stimulation techniques can overcome attention deficits, but these drugs or techniques are weakly beneficial in boosting the learning capabilities of healthy subjects. Here, we report a stimulation technique, mid-infrared modulation (MIM), that delivers mid-infrared light energy through the opened skull or even non-invasively through a thinned intact skull and can activate brain neurons in vivo without introducing any exogeneous gene. Using c-Fos immunohistochemistry, in vivo single-cell electrophysiology and two-photon Ca²⁺ imaging in mice, we demonstrate that MIM significantly induces firing activities of neurons in the targeted cortical area. Moreover, mice that receive MIM targeting to the auditory cortex during an auditory associative learning task exhibit a faster learning speed (~50% faster) than control mice. Together, this non-invasive, opsin-free MIM technique is demonstrated with potential for modulating neuronal activity.

Automatic sleep stage classification plays an essential role in sleep quality measurement and sleep disorder diagnosis. Although many approaches have been developed, most use only single-channel electroencephalogram signals for classification. Polysomnography (PSG) provides multiple channels of signal recording, enabling the use of the appropriate method to extract and integrate the information from different channels to achieve higher sleep staging performance. We present a transformer encoder-based model, MultiChannelSleepNet, for automatic sleep stage classification with multichannel PSG data, whose architecture is implemented based on the transformer encoder for single-channel feature extraction and multichannel feature fusion. In a single-channel feature extraction block, transformer encoders extract features from time-frequency images of each channel independently. Based on our integration strategy, the feature maps extracted from each channel are fused in the multichannel feature fusion block. Another set of transformer encoders further capture joint features, and a residual connection preserves the original information from each channel in this block. Experimental results on three publicly available datasets demonstrate that our method achieves higher classification performance than state-of-the-art techniques. MultiChannelSleepNet is an efficient method to extract and integrate the information from multichannel PSG data, which facilitates precision sleep staging in clinical applications. Source code is available at https://github.com/yangdai97/MultiChannelSleepNet .

The ability to remember and to navigate to safe places is necessary for survival. Place navigation is known to involve medial entorhinal cortex (MEC)-hippocampal connections. However, learning-dependent changes in neuronal activity in the distinct circuits remain unknown. Here, by using optic fiber photometry in freely behaving mice, we discovered the experience-dependent induction of a persistent-task-associated (PTA) activity. This PTA activity critically depends on learned visual cues and builds up selectively in the MEC layer II-dentate gyrus, but not in the MEC layer III-CA1 pathway, and its optogenetic suppression disrupts navigation to the target location. The findings suggest that the visual system, the MEC layer II, and the dentate gyrus are essential hubs of a memory circuit for visually guided navigation.