From Science Daily:

Using 20 years of functional magnetic resonance imaging (fMRI) data from tens of thousands of brain imaging experiments, computational neuroscientists Hava Siegelmann and a postdoctoral colleague at the University of Massachusetts have created a geometry-based method for massive data analysis to reach a new understanding of how thought arises from brain structure.

The authors say their work paves the way for advances in the identification and treatment of brain disease, as well as in deep learning artificial intelligence (AI) systems. Details appear in the current issue of Nature Scientific Reports.

As Siegelmann explains, fMRI detects changes in neural blood flow allowing researchers to relate brain activity with a cognitive behavior such as talking. She says, “The fMRI-based research did a wonderful job relating specific brain areas with activities. But no one ever tied together the tens of thousands of experiments performed over decades to show how the physical brain could give rise to abstract thought.”

She and colleagues found that cognitive function and abstract thought exist as an agglomeration of many cortical sources ranging from those close to sensory cortices to far deeper from them along the brain connectome, or connection wiring diagram. Siegelmann is director of the Biologically Inspired Neural and Dynamical Systems Laboratory at UMass Amherst and one of 16 recipients in 2015 of the National Science Foundation’s (NSF) Brain Research through Advancing Innovative Neurotechnologies (BRAIN) program initiated by President Obama to advance understanding of the brain.

The authors say their work demonstrates not only the basic operational…

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