A multi-scale extension of the Fisher information metric, derived from coarse-graining contraction rules, exactly captures the structure of mutual information in neural population codes and can be estimated via diffusion models.
Insights on representational similarity in neural networks with canonical correlation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.NC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A multi-scale information geometry reveals the structure of mutual information in neural populations
A multi-scale extension of the Fisher information metric, derived from coarse-graining contraction rules, exactly captures the structure of mutual information in neural population codes and can be estimated via diffusion models.