Stochastic resonance in an analog current-mode neuromorphic circuit

Stochastic resonance is a general phenomenon by which the sensitivity of a system to small inputs may be increased by the addition of noise. In this paper, we show that a neuro-inspired analog circuit naturally exhibits stochastic resonance. Transient circuit simulations allow the recognition of the evidence of this phenomenon. Detailed analyses show the importance of well choosing a specific neuronal parameter, the refractory period, so that the resonance can be used in practice. These results open the way for neuromorphic designs to process noisy data without signal processing, or to work in extremely noisy environments.