Firing behavior of hybrid scale-free neuronal networks

Information is often represented by the collective and distributed activity of a population of neurons. Various methods have been developed to analyze firing behavior to decode information represented by neuronal populations. In this context, the phenomenon of Inverse Stochastic Resonance ($ISR$) , where the average firing rate of a neuron is minimal respect to noise, has been studied in numerous studies at the single-neuron level or in various network topologies connected by electrical or chemical synapses. However, neuroimaging and electrophysiological studies have revealed the existence of hybrid architectures that incorporate these different synaptic components in functional neural circuits. In this study, neuronal firing behaviors are comprehensively examined at the level of a single neuron and a network when such a realistic hybrid coupling structure is in question. First, the average firing activity of a neuron of the network is analyzed depending on the ion channel noise and the importance of the ion channel blockage rate in the emergence of $ISR$ is highlighted. Then, the collective firing rate behavior of the hybrid network is examined, and the robustness of this phenomenon at the network level is ensured. The firing behavior that reveals such a phenomenon also provides critical preliminary information to explain the neuronal firing regularity and the synchronization between the neurons of the network. It is also suggested here that, considering $ISR$ behavior, neuronal populations in the hybrid structure exhibit a more stable firing behavior independent of network properties such as size and rewiring probability, synaptic effects such as synaptic time constant and network topology. Finally, it is stated that the $ISR$, which occurs at a constant current level close to the excitation threshold, disappears as it disappears from the threshold level.