Spectral Analysis of Representational Similarity with Limited Neurons

A recent study published on arXiv explores the challenges of measuring representational similarity in neuroscience due to the limitations of recording multiple neurons at once. By applying Random Matrix Theory, the researchers investigate how these constraints impact similarity measures like Centered Kernel Alignment and Canonical Correlation. This research is significant as it enhances our understanding of neural data analysis, potentially leading to better insights into brain function.