Advanced neuromorphic engineering approaches for restoring neural activity after brain injury: innovations in regenerative medicine

Restoring neural function after brain injury is a critical medical challenge, as conventional treatments often fail to achieve full recovery. This makes the development of innovative regenerative medicine and biomedical engineering strategies particularly necessary. This study aims to fill the existing gap in neuromorphic engineering by mimicking biological neuron dynamics and realizing effective clinical applications to promote functional recovery and quality of life enhancement in patients with brain injury. The novel neuromorphic engineering approaches leverage the dynamic behavior of brain neurons, incorporating electronic circuits that emulate neuronal dynamics. A basic configuration involves a neural model designed to mimic the dynamics of a living neuron, with the potential to replace damaged brain tissue when implanted, thus restoring signal propagation. An enhanced configuration integrates a closed-loop system, wherein the feedback signal from biological neurons synchronizes the artificial neuron with its living counterpart, allowing continuous self-adjustment of system parameters and promoting a neuro-autogenerative regime. Further refinement introduces a memristive device that connects artificial neurons to simulate synaptic plasticity. In conjunction with feedback control, this system enables self-tuning of parameters for improved adaptability to natural neural dynamics. The supporting software combines nonlinear dynamics with deep learning techniques, specifically employing reservoir computing for enhanced performance. These approaches were successfully validated in vitro and in vivo using mice models, demonstrating that advanced neuromorphic engineering holds significant potential for restoring neural activity after brain injury. This offers a promising strategy in regenerative medicine to improve rehabilitation outcomes and patients’ life quality by aiding neurological function reconstruction.