Enhancing Signal Resolution in a Noisy Nonlinear Sensor

It is well known that the resolution (defined as the smallest change in the signal being detected or quantified) of a sensor can be improved by increasing the observation time \(T\) of the measurement; typically, the resolution scales as \(1/T^a\). Typically \(a=0.5\), or \(a<0.5\) if low frequency noise is present. We show that a neuronal system can display an enhanced scaling in the resolution, with the parameter \(a=1\); this occurs when the “inter spike intervals” are negatively correlated. We also show that, by introducing negative correlations into the time domain response of a nonlinear dynamical sensor, it is possible to replicate this enhanced scaling. This affords us the possibility of designing a novel class of biomimetic sensors that results in improved signal resolution by functionally utilizing negative correlations.