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Updated: May 12, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Summary statistics of learning link changing neural representations to behavior
Jacob A Zavatone-Veth1,2, Blake Bordelon3,1, Cengiz Pehlevan3,1,4
1Center for Brain Science, Harvard University, Cambridge, MA, USA.
Abstract:
How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.
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