@article {49534,
	title = {High-dimensional geometry of population responses in visual cortex.},
	journal = {Nature},
	volume = {571},
	year = {2019},
	month = {2019 Jun 26},
	pages = {361-65},
	abstract = {<p>A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.</p>
},
	doi = {10.1038/s41586-019-1346-5},
	author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Carandini, Matteo and Harris, Kenneth D}
}
