de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

The effect of noise correlations in populations of diversely tuned neurons

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons75429

Ecker,  A. S.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

Berens,  P.
Max Planck Society;

Tolias,  A. S.
Max Planck Society;

Bethge,  M.
Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Ecker, A. S., Berens, P., Tolias, A. S., & Bethge, M. (2011). The effect of noise correlations in populations of diversely tuned neurons. Journal of Neuroscience, 31(40), 14272-14283. doi:DOI:10.1523/JNEUROSCI.2539-11.2011.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4C32-6
Abstract
The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.