The effect of pairwise neural correlations on global population statistics


Simultaneously recorded neurons often exhibit correlations in their spiking activity. These correlations shape the statistical structure of the population activity, and can lead to substantial redundancy across neurons. Here, we study the effect of pairwise correlations on the population spike count statistics and redundancy in populations of threshold-neurons in which response-correlations arise from correlated Gaussian inputs. We investigate the scaling of the redundancy as the population size is increased, and compare the asymptotic redundancy in our models to the corresponding maximum-and minimum entropy models.

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.