In-silico Modeling for Neural Interfacing

Develop computational models to improve human-machine link and to unveil the underlying mechanisms

We develop computational and hybrid models of the peripheral and central nervous systems to improve the interaction between humans and neuroprosthetic technologies and to uncover the neural mechanisms underlying sensorimotor function.

Our work combines data-driven, biophysical, and machine-learning approaches to model neural activity across multiple scales, from peripheral nerves to cortical circuits. These models are used to better decode neural signals, design more effective sensory stimulation, predict neural responses, and optimize the interaction between the nervous system and artificial devices.

By integrating experimental data with computational models, we aim to develop more efficient, adaptive, and biologically informed neural interfaces, while gaining new insights into how the nervous system encodes and processes movement and sensation.