Detection of Time Varying Signals by the Dissipative Stochastic Mechanics Based Neuron Model

Abstract

Led by the presence of a multiple number of gates in an ion channel, it was recently put forward that the equations of activity for the neuronal dynamics acquire some renormalization terms when the underlying membrane is of limited size [1], and that these terms can play a significant role in the dynamics of small size excitable membranes [2]. In this study, we examine the resultant computational neuron model, from the above approach, in the case of time varying input currents. In particular, we focus on what role the renormalization terms might be playing in the signal-to-noise ratio. Our investigation reveals that the presence of renormalization terms enhances the capability of the model in detecting weak periodic currents by causing an increase in the signal-to-noise ratio value.