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December 18, 2015 06:00 pm

How Brain Architecture Leads To Abstract Thought

catchblue22 writes: UMass Amherst scientists have analyzed fMRI data to link brain architecture with consciousness and abstract thought. "We momentarily thought our research failed when we saw that each cognitive behavior showed activity through many network depths. Then we realized that cognition is far richer, it wasn't the simple hierarchy that everyone was looking for. So, we developed our geometrical 'slope' algorithm," said neuroscientist Hava Siegelmann (abstract). "With a slope identifier, behaviors could now be ordered by their relative depth activity with no human intervention or bias," she adds. They ranked slopes for all cognitive behaviors from the fMRI databases from negative to positive and found that they ordered from more tangible to highly abstract. "'Deep learning is a computational system employing a multi-layered neural net...the brain's processing dynamic is far richer and less constrained because it has recurrent interconnection, sometimes called feedback loops.' Her lab is now creating a 'massively recurrent deep learning network,' she says, for a more brain-like and superior learning AI."

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