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About this paper
Mehrad Sarmashghi, Shantanu P. Jadhav, Uri T. Eden
The authors combine point-process GLMs with an MCDCNN to classify CA1 neurons by coding influences. GLM-based model selection assigns categories and generates synthetic examples, using either theoretical parameter distributions or estimates from observed neurons. On a three-class test set containing simulated and observed examples, empirical augmentation reached 91.6% accuracy versus 78.43% for balanced augmentation, demonstrating performance in this designed setting.
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