Robust information propagation through noisy neural circuits
Fig 7
Not all synergistic population codes are equally robust against corruption by noise.
This figure is similar to Fig 2, but with synergistic instead of redundant population codes. We constructed two model populations—each with the same 100 tuning curves (20 randomly-chosen example tuning curves are shown in panel A)—for the first layer of cells. The two populations have different covariance structures Σξ for their trial-to-trial variability (see main text, Eq (4)), but convey identical amounts of information, Ix(s), about the stimulus. (B) We corrupted the responses of each neural population by Gaussian noise (independently and identically distributed for all cells) of variance σ2, to mimic corruption that might arise as the signals propagate through a multi-layered neural circuit, and computed the output information, Iy(s), that these further-corrupted responses convey about the stimulus (blue and green curves). (C) Input information Ix(s) in the two model populations (left; “correlated”) and information that would be conveyed by the model populations if they had their same tuning curves and levels of trial-to-trial variability, but no correlations between cells (right; “trial-shuffled”). For panels B and C, we computed the information for 100 different stimulus values, equally spaced between 0 and 2π, and averaged the information over these stimuli.