Nikolay Koshev

The paper models cross-excitation in dorsal root ganglia to show it improves spatial localization at the cost of intensity encoding, leveraging the Weber-Fechner law.

The paper tests diffusion and gap-junction hypotheses for cross-excitation in DRG neurons and finds neither adequately explains the phenomenon.

The paper evaluates the performance of yttrium-iron garnet magnetometers in MEG by analyzing lead-field matrices and comparing signal metrics to existing systems.

The paper presents a first solid-state fluxgate magnetometer for magnetoencephalography (MEG) and validates its feasibility by detecting the human alpha rhythm.

This paper introduces deep learning methods to improve EEG spatial resolution by translating scalp potentials to cortical sources using CNN-based solvers trained on synthetic data.