Speaker
Description
Sensory neurons collectively encode stimulus information through patterns of correlated activity. A key — yet theoretically unresolved — observation is that neurons with similar stimulus tuning consistently show the strongest noise correlations. Existing frameworks fail to explain this structure, leaving a fundamental gap in our understanding.
We address this gap by introducing a fully geometric framework that characterizes how noise correlations influence stimulus encoding. In low-noise regimes, our approach unifies and extends classical results, including the Sign Rule and the detrimental role of information-limiting correlations, by recasting them in terms of the intrinsic geometry of the signal manifold. In high-noise regimes, the picture becomes substantially richer: we identify the geometric conditions under which noise correlations either enhance or impair coding fidelity. Strikingly, strong noise correlations can be beneficial, even when locally aligned with the signal manifold; a result that challenges conventional wisdom.
These findings revise and deepen our understanding of population coding in low-dimensional stimulus spaces, while providing a principled foundation for extending the analysis to high-dimensional settings.
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