Current speech brain-computer interfaces (BCIs) rely on patient-specific decoding approaches. Here, the authors show that patient-specific data can be aligned to a shared space that preserves speech i...

Direct recordings from the human brain reveal a hierarchy in planning to speak, with neural activity organizing whole syllables before the individual sounds that compose them are sequenced.

MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.

Liu et al. examine the role of sustained neural activity in the planning and production of speech sequences, revealing a key role for the middle precentral gyrus.

Debilitating anxiety is pervasive in the modern world. Choices to approach or avoid are common in everyday life and excessive avoidance is a cardinal feature of all anxiety disorders. Here, we used in...

Calcium imaging of spontaneously behaving mice show increased intrinsic neural timescales during behavior. The behavioral state of mice can be predicted from the topography of timescales of the cortex...

Working memory is constructive in nature. In three electroencephalography/magnetoencephalography studies, Fan et al. provide converging behavioural and neural evidence that a one-dimensional syllable ...

Hippocampal theta–gamma phase–amplitude coupling integrates cognitive control and working memory storage across brain areas in humans.

Prior lesion, noninvasive-imaging, and intracranial-electroencephalography (iEEG) studies have documented hierarchical, parallel, and distributed characteristics of human speech processing. Yet, there...

Speech brain-computer interfaces show great promise in restoring communication for people who can no longer speak[1][1]–[3][2], but have also raised privacy concerns regarding their potential to decod...

It is unknown how cortical stimulation identifies brain regions critical to speech and language when they depend upon broader brain networks. Here the authors show that these critical areas function a...

How sensory evidence is transformed into motor output is not fully understood. Here, the authors use stereoEEG recordings during a rule-switching task to reveal network dynamics of decision-making.

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrins…

Information stored in memory is selectively routed to the human frontal cortex only when a decision requires access to memory.

Multilingual articulatory representations in the speech-motor cortex of a participant with vocal-tract and limb paralysis enabled the development of a bilingual speech neuroprosthesis.

Neural activity does not always lie in a low-dimensional subspace. The authors extend this classic view to show that task-relevant information is distributed across multiple covariability classes and ...

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

The authors show that neuronal populations in the human prefrontal-motor network interact via two discernible communication modes – ramping dynamics and neural oscillations. These modes operate in con...

Here, using neural activity patterns in the inferior frontal gyrus and large language modeling embeddings, the authors provide evidence for a common neural code for language processing.

Using direct intracranial recordings and modern speech AI models, Li and colleagues show representational and computational similarities between deep neural networks for self-supervised speech learnin...

High-density single-neuron recordings show diverse tuning for acoustic and phonetic features across layers in human auditory speech cortex.

In the past 20 years, the field of bilingualism has made a substantial effort to better understand the set of cognitive mechanisms that allow bilingua…