Improved Estimation and Interpretation of Correlations in Neural Circuits

Explore this paper's citation graph

Summary

It is hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs, and in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators.

Type
article
Published
2015-03-01
Cited by
103
References
108
Access
Open access

Keywords

Estimator, Pairwise comparison, Covariance matrix, Correlation, Artificial neural network

References

Cited by

Related papers