Information transmission in biological feedforward neuronal networks

Information processing in the nervous system involves multiple stages of neuronal networks, where neuronal activity progress from one sub-population to another. Computational approaches provide useful tools to understand the underlying mechanisms of the activity propagation through multiple processing stages. A feedforward sequence of layers of neurons provides a simple platform for analyzing the propagation of neuronal activity in the nervous system.The present work aims at a better understanding of neuronal activity propagation in Feedforward Networks (FFN) by including a more biophysically realistic model of individual neurons on the network, where the stochastic behavior of voltage-gated ion channels embedded in neuronal membranes is modeled depending on the cell size. First, it is determined under which conditions the propagation of weak periodic signals through a FFN is optimal. It is found that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the FFN, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted.Next, in the context of rate coding, firing rate propagation is studied in FFN by considering the different noise regimes in layers. For all regimes, the stable propagation of input firing rate through the network can be achieved via the synchronization mechanism within the neurons in layers. It is also shown that the development of this mechanism in the network depends on the input rate, interlayer-link density, synaptic current statistics and intrinsic noise intensity in layers.Achieved results are consistent with experimental results, given in the literature, and advance our understanding of how information is processed in FFN.