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To survive in a complex social group, one needs to know who to approach and, more importantly, who to avoid. In mice, a single defeat causes the losing mouse to stay away from the winner for weeks1. Here through a series of functional manipulation and recording experiments, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin-receptor-expressing cells in the anterior subdivision of the ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance. Before defeat, aVMHvlOXTR cells minimally respond to...

In this CME article, review the current treatment modalities for noninvasive brain stimulation in the treatment of substance use disorders, and to explore the potential for further expansion of indications.

Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays1–3 to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus4,5, while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative v...

The stimulation of deep brain structures has thus far only been possible with invasive methods. Transcranial electrical temporal interference stimulation (tTIS) is a novel, noninvasive technology that might overcome this limitation. The initial proof-of-concept was obtained through modeling, physics experiments and rodent models. Here we show successful noninvasive neuromodulation of the striatum via tTIS in humans using computational modeling, functional magnetic resonance imaging studies and behavioral evaluations. Theta-burst patterned striatal tTIS increased activity in the striatum and as...

Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today’s dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computation...