A comprehensive, data-driven and interpretable nonlinear computational modeling framework based on deep neural networks uncovers different nonlinear transformations of speech signal in the human auditory cortex.
Hyperalignment provides a conceptual framework for cortical architecture that captures how shared information is encoded in idiosyncratic cortical topographies that preserve vector geometry for population response and connectivity patterns.
When coupling between STN spikes and cortical gamma oscillations was strong, subsequent movement was initiated earlier, independent of changes in mean firing rates, demonstrating the importance of relative spike timing.
An implantable device based on organic electrochemical transistors is developed for quantitative mapping of neurotransmitter release across multiple brain regions, revealing a cross-talk between the mesolimbic and nigrostriatal dopaminergic pathways.
Direct insular recordings in humans reveal that contrary to several prominent models of speech production, it is not engaged in pre-articulatory planning, but in auditory and somatosensory components of speech.