Researchers modeled artificial neural networks based on human brains for performing cognitive tasks efficiently.
Our brains are the most efficient networks that can perform complex tasks efficiently. Researchers from McGill University suggest that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently.
Researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). The ANN is a computational network based on biological neural networks that construct the structure of the human brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.
By combining brain connectomics, study of brain organization, into the construction of ANN architectures, researchers hoped to both learn how the wiring of the brain supports specific cognitive skills, and to derive novel design principles for artificial networks. They found that ANNs with human brain connectivity can perform cognitive memory tasks more flexibly and efficiently than other benchmark architectures.
“The project unifies two vibrant and fast-paced scientific disciplines,” says Bratislav Misic, a researcher at The Neuro and the paper’s senior author. “Neuroscience and AI share common roots, but have recently diverged. Using artificial networks will help us to understand how brain structure supports brain function. In turn, using empirical data to make neural networks will reveal design principles for building better AI. So, the two will help inform each other and enrich our understanding of the brain.”
The study is published in the journal Nature Machine Intelligence.
