Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit
Fig 5
Comparing the performance (measured through network gain) of nonlinear and linear feedback inhibitory networks (measured through network gain: lower is better) (details in Methods).
(a) Two input mixtures, modeling rapid transition from predictable to unpredictable input components. (b) Description of simulations. Inputs constructed as in (a). At each time point, inputs are either pure predictable signal, or pure unpredictable noise, with an instantaneous transition from one type to the other, in the middle of the simulation period. (c-f) Simulation outputs (inputs shown in inset). The amplitude of the unpredictable component of the mixture varies along the x axis. Error bars are 1 std. dev. (c,e) Network gain of the linear network of type 1, optimized to the mixture (blue, non-adapted linear response), is significantly higher than that of the nonlinear network (red). In contrast, the nonlinear network gain is close to the response of the optimal linear network of type 2, which is allowed to adapt to each component of the mixture (dotted black, adapted linear response). (e) Green shading indicates region where the nonlinear response is more than one std. dev. lower than the non-adapted linear response. Diagonal hashing indicates region where the nonlinear response is within one std. dev. of the adapted linear response. (d,f) % improvement of the performance of the nonlinear network over the type 1 linear network at different amplitudes of the unpredictable component. Data taken from (c) and (e) respectively. (f) Green box indicates region where the improvement is more than one std. dev. different from 0.