Population encoding of observed and actual somatosensations in the human posterior parietal cortex.

Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations.

Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters.

Aperiodic (1/f) Neural Activity Robustly Tracks Symptom Severity Changes in Treatment-Resistant Depression.

Background: A reliable physiological biomarker for major depressive disorder is essential for developing and optimizing neuromodulatory treatment paradigms. In this study, we investigated a passive electrophysiologic biomarker that tracks changes in depressive symptom severity on the order of minutes to hours.

Methods: We analyzed brief recordings from intracranial electrodes implanted deep in the brain during a clinical trial of deep brain stimulation for treatment-resistant depression in 5 human participants (n = 3, n = 2).

An AI-Driven Model of Consciousness, Its Disorders, and Their Treatment.

Understanding the neural signatures of consciousness and the mechanisms underlying its disorders, such as coma and unresponsive wakefulness syndrome, remains a critical challenge in neuroscience. In this study, we present a novel computational approach for the in silico discovery of neural correlates of consciousness, the mechanisms driving its disorders, and potential treatment strategies. Inspired by generative adversarial networks, which have driven recent advancements in generative artificial intelligence (AI), we trained deep neural networks to detect consciousness across multiple brain areas and species, including humans.