Optimal E:I ratio for coding fidelity.

(top row) A sparse coding model is placed under a volume constraint by restricting the total number of neurons to N. Excitatory neurons receive recurrent as well as feed forward (stimulus) input and are responsible for coding the stimulus. Inhibitory interneurons are driven by recurrent excitatory inputs, and enable accurate computation of the neural encoding to induce sparsity in the excitatory neurons. (middle row) We vary the relative size of the excitatory (N_E) and inhibitory (N_I) subpopulations and evaluate the model at different E:I ratios under the volume constraint, N = N_E + N_I. (bottom row) We show that coding fidelity is optimal (boxed image at 6:1) at a unique, biologically plausible E:I ratio for the fixed volume. We evaluate models coding 16 × 16 = 256 pixel natural image patches [37] with N = 1200 (≈ 5× overcomplete representation).