Is it possible to reconstruct what someone sees based on brain signals alone? The answer is no, not yet. But EPFL researchers have made a step in that direction by introducing a new algorithm for building artificial neural network models that capture brain dynamics with an impressive degree of accuracy.

 

Rooted in mathematics, the novel machine learning algorithm is called CEBRA (pronounced zebra), and learns the hidden structure in the neural code.

 

What information the CEBRA learns from the raw neural data can be tested after training by decoding -- a method that is used for brain-machine-interfaces (BMIs) -- and they've shown they can decode from the model what a mouse sees while it watches a movie. But CEBRA is not limited to visual cortex neurons, or even brain data. Their study also shows it can be used to predict the movements of the arm in primates, and to reconstruct the positions of rats as they freely run around an arena. The study is published in Nature.