Google researchers create AI that maps the brain's neurons

Mapping the structure of biological networks in the nervous system -- a field of study known as connectomics -- is computationally intensive. The human brain contains around 86 billion neurons networked through 100 trillion synapses, and imaging a single cubic millimeter of tissue can generate more than 1,000 terabytes of data. Luckily, artificial intelligence can help. In a paper (High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks) published in the journal Nature Methods, scientists at Google and the Max Planck Institute of Neurobiology demonstrated a recurrent neural network -- a type of machine learning algorithm that's often used in handwriting and speech recognition -- tailored made for connectomics analysis. Google researchers aren't the first to apply machine learning to connectomics -- in March, Intel partnered with the Massachusetts Institute of Technology's Computer Science and AI Laboratory to develop a "next-gen" brain image processing pipeline.