Stanford University

A review published on August 20, 2026 in npj Biomedical Innovations proposes brain-inspired brain-computer interfaces (BI-BCIs), a framework that unifies neuromorphic computing with BCI design to make neurotechnology lower-power, smaller and capable of closed-loop operation. The authors, from Aarhus University, Stanford University, the University of Southern Denmark, the University of Genoa, Forschungszentrum Jülich and RWTH Aachen University, say the approach could advance neuroprosthetics and neuromodulation for neurological disorders.

A Stanford program is recording brain activity in psychiatric inpatients at millisecond precision, combining noninvasive electrode arrays with deep brain recording electrodes to trace the circuits behind schizoaffective disorder, borderline personality disorder, autism and cancer-induced depression. The Human Neural Circuitry program, led by neuroscientist Karl Deisseroth at the Wu Tsai Neurosciences Institute, has run for three years and moves data over fiber-optic and copper links fast enough for round trips of under half a millisecond, allowing the system to sense and respond in a closed loop. Deisseroth said the key innovation is getting results at millisecond precision, which he called a crucial step toward understanding complex psychiatric symptoms.

Researchers at Coherence Neuro Global, Inc. and other institutions have used implanted neural interfaces to track glioma growth in freely behaving mice over time. Across several mouse strains and glioma models, tumor progression went with elevated gamma-band activity in the tumor microenvironment, and machine learning models read tumor burden off the chronic recordings, with gamma trajectories predicting individual growth rates. The work was posted to bioRxiv on July 9, 2026, and has not been peer reviewed.