Mathematical Equivalence of Two Common Forms of Firing Rate Models of Neural Networks

Kenneth D. Miller, Francesco Fumarola

Abstract: We demonstrate the mathematical equivalence of two commonly used forms of firing rate model equations for neural networks. In addition, we show that what is commonly interpreted as the firing rate in one form of model may be better interpreted as a low-pass-filtered firing rate, and we point out a conductance-based firing rate model.