An Investigation into the Dissipative Stochastic Mechanics Based Neuron Model under Time Varying Input Currents

Led by the presence of a multiple number of gates in an ion channel, it was recently predicted that the equations of activity for the neuronal dynamics acquire some renormalization terms which play a significant role in the dynamics for smaller membrane sizes (Güler 2006, 2007, 2008). In this Thesis, 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 values. Our investigation reveals that the presence of renormalization terms somehow enhances the signal-to-noise ratio.