We are pleased to announce that a publication co-authored by Dr. Volodymyr Cherkas from our Institute has appeared in the prestigious journal Nature Communications.

Title: Non-genetic neuromodulation with graphene optoelectronic actuators for disease models, stem cell maturation, and biohybrid robotics

Authors: Elena Molokanova, Teng Zhou, Pragna Vasupal, Volodymyr P. Cherkas, Prashant Narute, Mariana S. A. Ferraz, Michael Reiss, Angels Almenar-Queralt, Georgia Chaldaiopoulou, Janaina Sena de Souza, Honieh Hemati, Francisco Downey, Omowuyi O. Olajide, Carolina Thörn Perez, Francesca Puppo, Pinar Mesci, Samuel L. Pfaff, Dmitry Kireev, Alysson R. Muotri & Alex Savchenko

Graphene-Based Biohybrid Interfaces Enable Intelligent Neural Computation and Robotic Control. Unique electrical and optical properties of graphene enable its use in biocompatible optoelectronic interfaces that can precisely interact with living neural tissue. In this pioneering study, published in Nature Communications (2025), the authors demonstrate that neuronal organoids grown on graphene interfaces can be trained to perform computational tasks through their own intrinsic neural circuitry. These organoids received sensory input from conventional sensors and generated control signals driving a robotic system – achieving  a closed-loop brain-machine interaction without any genetic modification.

The work introduces a new paradigm for non-genetic neuromodulation and lays the foundation for biohybrid intelligence, where living neural networks and artificial devices cooperate in real time.

This breakthrough became possible through long-standing international collaboration, combining expertise in materials science, neuroscience, bioengineering and bioinformatics – a model of interdisciplinary innovation driving the next generation of neural interfaces.

Link to the article: https://www.nature.com/articles/s41467-025-62637-6

Link to the movie:
https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62637-6/MediaObjects/41467_2025_62637_MOESM6_ESM.mp4