Fig. 6: Dynamical adaptation of feedback strength.
a, Schematic describing the dynamic adaptation algorithm used in b and c. b, Time series of sensor signals for two different sound amplitudes (blue and red) obtained from experiments with the FPGA-based implementation of feedback strength switching (schematic shown in a) between a1 = 0.7 and a0 = 0. Here the feedback strength is kept at its lower value for a constant time interval τ2 before resetting it to the high-sensitivity regime. This yields a spike-like response of the sensor system to the constant sound input. The comparison for both sound amplitudes reveal an amplitude-dependent spike rate, which is determined by the sound-amplitude-dependent part τ1 and the fixed time interval τ2 for reset. c, Numerical implementation of sensory adaptation obtained from LTspice simulations of system with adaptation circuit (schematic shown in a). The envelope of the sensor signal with dynamic adaptation of feedback strength is shown in the case of a constant sound input in the interval of 0.05 to 0.10 s. Switching events changing the feedback strength are marked with the blue dashed lines. The dynamic adaptation increases the resolution and dynamic range by enabling the sensing of low sound pressures before switching (nonlinear regime a1 = 0.8), which are otherwise below the noise level, and the discrimination of large sound amplitudes after switching (linear regime a0 = 0.5). The resolution decreases with increasing sound amplitude and large sound amplitudes can drive the sensor into saturation in the nonlinear regime (Fig. 3a). d, Peak (black dots) and plateau (red squares) amplitude of the time series obtained from dynamic adaptation simulations shown in c: a decreasing resolution with increasing sound amplitude is observed for the peak amplitudes due to the nonlinear-operation range (a1 = 0.8). In contrast, for the linear-operation regime (a0 = 0.5), the resolution remains constant. The dashed curves are fits as a guide to the eye.
