In a significant step towards understanding and decoding brain signals, researchers at the École Polytechnique Fédérale de Lausanne (EPFL) have developed an innovative machine learning algorithm called CEBRA.
This algorithm is capable of creating artificial neural network models that capture brain dynamics with impressive precision, bringing us closer to reconstructing what a subject sees based solely on brain signals.
CEBRA, mathematical marvel
Using this unique algorithm, EPFL researchers have shown that they can decode what a mouse sees while watching a movie. The data used for video decoding was obtained through the Allen Institute and consisted of recorded brain signals, or acquired directly by measuring brain activity via electrode or optical probes inserted into the visual cortex area of the mouse brain.
The ability ofalgorithm of reconstructing synthetic data far surpasses all other existing algorithms. Take a look for yourself.
EPFL goes beyond neuroscience
CEBRA's potential applications go beyond neuroscience. This algorithm could be applied to any data set, such as animal behavior or gene expression data. This opens up exciting potential clinical applications, positioning CEBRA as a major contributor in our journey to understanding complex systems such as the brain.
To be more precise? This study by the EPFL team, led by Mackenzie Mathis, represents a significant step towards high-performance brain-machine interfaces (BMIs). A promising platform for discovering new principles in neuroscience. And, for lovers of retrothinking, a method to transform even animals into potential "cameras". The research was published in the prestigious scientific journal Nature (I link it to you here), and marks a decisive turning point in this field.
In short (Italian only)
The CEBRA algorithm represents a leap forward in our understanding of how the brain processes visual information. The potential for using this knowledge in practical applications (as always, for better or worse) is immense.
The combination of neuroscience and machine learning have only begun to show us the film of the future.
