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Abstract #3137

A human brain number system model based on fMRI connectivity and deep-learning network

Ray F. Lee1
1Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Brain’s number system is essential for humans to gauge magnitudes, but has not been explicitly explained. An fMRI experiment was designed to quantitatively measure BOLD responses during numbering processes. The functional connectivity elucidates a deep-learning network model that decomposes the numbering to subitizing and counting, where subitizing is modeled as a CNN, and counting is modeled as an RNN where the subitizing is its recurrent cell. The initial success rate of this model is >95% for subitizing and >90% for counting in 500 tests. Further matching between the brain parcellates and the recurrent cell may fully reveal our number system.

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