High-Fidelity Coding with Correlated Neurons

Rava Azeredo da Silveira (1, 2, 3) , Michael J. Berry Ii (2)

1 LPS - Laboratoire de Physique Statistique de l'ENS
2 Princeton University
3 FRDPENS - Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris

Positive correlations in the activity of neurons are widely observed in the brain. Previous studies have shown these correlations to be detrimental to the fidelity of population codes, or at best marginally favorable compared to independent codes. Here, we show that positive correlations can enhance coding performance by astronomical factors. Specifically, the probability of discrimination error can be suppressed by many orders of magnitude. Likewise, the number of stimuli encoded—the capacity—can be enhanced more than tenfold. These effects do not necessitate unrealistic correlation values, and can occur for populations with a few tens of neurons. We further show that both effects benefit from heterogeneity commonly seen in population activity. Error suppression and capacity enhancement rest upon a pattern of correlation. Tuning of one or several effective parameters can yield a limit of perfect coding: the corresponding pattern of positive correlation leads to a 'lock-in' of response probabilities that eliminates variability in the subspace relevant for stimulus discrimination. We discuss the nature of this pattern and we suggest experimental tests to identify it.

Domaines: Sciences du Vivant [q-bio]

Rava Azeredo da Silveira, Michael J. Berry Ii. High-Fidelity Coding with Correlated Neurons. PLoS Computational Biology, 2014, 10 (11), pp.e1003970. ⟨10.1371/journal.pcbi.1003970⟩. ⟨hal-01342595⟩