Disorder Induced Resonance

Stochastic Resonance gainned much attention in the last decades, due to the counter-intuitive interplay between a weak signal and noise.
This phenomenon is present in non-linear systems and its signature is the fact that the (otherwise undetectable) signal can be amplified by means of the noise, but only at an intermediate noise value. When the noise intensity is too large or too weak, such amplification disappears. It has been demonstrated that a large variety of systems exhibit this phenomenon. It has been even argued that this phenomenon might be responsible for the detection of small signals, for example, in different regions of the brain, although in most situations noise can not be tuned to the optimal value. We find, however, that the natural diversity between the neurons (often neglected in neural models) can play a role of a kind of {\\it internal noise}.

To understand the role of diversity in an ensemble of excitable units we study an extended systems of fully connected FitzHugh-Nagumo units, as a simple approximation of the highly connected population of neurons in a region of the brain. We show that a similar phenomenon to that of Stochastic Resonance appears in excitable and bistable systems when considering diversity: there exists an optimum value of diversity in system parameters such that the response of the whole system to an external signal is optimal.