Journal
JOURNAL OF NEUROSCIENCE
Volume 31, Issue 40, Pages 14272-14283Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.2539-11.2011
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Funding
- German Ministry of Education, Science, Research, and Technology (Bundesministerium fur Bildung und Forschung) [FKZ 01GQ0601]
- German Excellency Initiative through the Centre for Integrative Neuroscience Tubingen
- Max Planck Society
- German National Academic Foundation
- National Eye Institute-National Institutes of Health [R01 EY018847]
- Arnold and Mabel Beckman Foundation
- Veterans Affairs Merit Award
- McKnight Endowment Fund for Neuroscience Scholar Award
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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.
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