Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network

Percy K. Mistry (), Anthony Strock, Ruizhe Liu, Griffin Young and Vinod Menon ()
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Percy K. Mistry: Stanford University School of Medicine
Anthony Strock: Stanford University School of Medicine
Ruizhe Liu: Stanford University School of Medicine
Griffin Young: Stanford University School of Medicine
Vinod Menon: Stanford University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-21

Abstract: Abstract Number sense, the ability to decipher quantity, forms the foundation for mathematical cognition. How number sense emerges with learning is, however, not known. Here we use a biologically-inspired neural architecture comprising cortical layers V1, V2, V3, and intraparietal sulcus (IPS) to investigate how neural representations change with numerosity training. Learning dramatically reorganized neuronal tuning properties at both the single unit and population levels, resulting in the emergence of sharply-tuned representations of numerosity in the IPS layer. Ablation analysis revealed that spontaneous number neurons observed prior to learning were not critical to formation of number representations post-learning. Crucially, multidimensional scaling of population responses revealed the emergence of absolute and relative magnitude representations of quantity, including mid-point anchoring. These learnt representations may underlie changes from logarithmic to cyclic and linear mental number lines that are characteristic of number sense development in humans. Our findings elucidate mechanisms by which learning builds novel representations supporting number sense.

Date: 2023
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DOI: 10.1038/s41467-023-39548-5

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