Machine Learning Harnessed To Build Map of the Connectome

Technology Networks | November 08, 2019

The brain is considered to be one of the most complex systems in existence. And while significant headway has been made to understand it, we tend to generate more questions than answers. But now a research team led by Kyoto University has developed a machine learning model that allows scientists to reconstruct neuronal circuitry by measuring signals from the neurons themselves. The model has the potential to elucidate the difference in neuronal computation in different brain regions. To comprehend the brain, we must look at the neurons that construct it. Our entire world of perception runs across these billions of cells in our head. And that is compounded by the exponentially larger number of connections -- known as synapses -- between them, making the path to our understanding a challenge. Shigeru Shinomoto from Kyoto University's School of Science, who headed the project, explains that although it is possible to record the activity of individual neurons in the brain -- and that number has increased dramatically over the last decade -- it is still a challenge to map out how each of these cells connects to each other.

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Vaxess Technologies | October 27, 2021

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RESEARCH

Chemistry42, an AI System from Insilico, has been Incorporated into UCB's Drug Discovery Programmes

Insilico Medicine | March 15, 2021

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INDUSTRIAL IMPACT

MeMed raises $93M to accelerate commercialization of its host immune-response product portfolio

MeMed | January 10, 2022

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