Propagation of firing rate in a noisy feedforward biological neural network

In this study, we investigate the input firing rate propagation in a feedforward biological neural network composed of multiple layers. Dynamical behaviour of neurons in the network are modeled by using stochastic Hodgkin-Huxley equations which considers the probabilistic nature of ion channels embedded in neuronal membranes. Thus, firing rate propagation is studied in a biophysically more realistic manner by including ion channel noise which is ignored in previous studies. Input rate information in the network is provided by varying the cell size in the first layer. We show that the efficent transmission of input firing rate through the network can be achieved via the synchronization mechanism within the neurons in layers. We also show that this synchronization araise from the synaptic current variance increase and provided by adjusting the cell size or the intrinsic channel noise strength in layers to an optimal value.