Fig. 2.

Information transmission in a network of two neurons. (A) Schematic of a two-neuron network, {σ₁,σ₂}, coupled with strength J, receiving correlated binary or Gaussian inputs. α = Cov(h) = input correlation; Cov(σ) = 〈σ₁σ₂〉-〈σ₁〉〈σ₂〉 = correlation between output spike trains. (B) Optimal J^∗ as a function of input correlation, Cov(h), and neural reliability β for binary inputs. (C) Optimal J^∗ as a function of input correlation and neural reliability for Gaussian inputs. (D) J^∗ as a function of input correlation for three values of reliability (β = 0.5, 1, 2, grayscale) and Gaussian inputs; these are three horizontal sections through the diagram in C. At high reliability the optimal J^∗ has an opposite sign to the input correlation; at low reliability it has the same sign. (E) Output correlation as a function of input correlation and reliability for Gaussian inputs. At high reliability (β = 2) the network decorrelates the inputs. At low reliability (β = 1/2) the input correlation is enhanced. (F) Fractional improvement in information transmission in optimal (J^∗) vs. uncoupled (J = 0) networks.