2013/07/12 by Rava Azeredo da Silveira, Michael J. Berry, da Silveira, Rava Azeredo +2 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Physics and Astronomy · #FOS: Biological sciences #Neural Networks and Applications #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.1307.3591
Includes the Supplementary Material, as well as 7 figures and 3 supplementary figures
openalex publication_date 2013/07/12 · arxiv created 2013/07/19 · arxiv updated 2013/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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 by similarly large factors. 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. In the limit of perfect coding, this pattern 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 suggest experimental tests to identify it.