Impact of the Excitatory-Inhibitory Neurons Ratio on Scale-Free Dynamics in a Leaky Integrate-and-Fire Model

Abstract

ABSTRACTThe relationship between ratios of excitatory to inhibitory neurons and the brain’s dynamic range of cortical activity is crucial. However, its full understanding within the context of cortical scale-free dynamics remains an ongoing investigation. To provide insightful observations that can improve the current understanding of this impact, and based on studies indicating that a fully excitatory neural network can induce critical behavior under the influence of noise, it is essential to investigate the effects of varying inhibition within this network. Here, the impact of varying inhibitory-excitatory neuron ratios on neural avalanches and phase transition diagrams, considering a range of synaptic efficacies in a leaky integrate-and-fire model network, is examined. Our computational results show that the network exhibits critical, sub-critical, and super-critical behavior across different synaptic efficacies. In particular, a certain excitatory/inhibitory (E/I) ratio leads to a significantly extended dynamic range compared to higher or lower levels of inhibition and increases the probability of the system being in the critical regime. In this study, we used the Kuramoto order parameter and implemented a finite-size scaling analysis to determine the critical exponents associated with this transition. To characterize the criticality, we studied the distribution of neuronal avalanches at the critical point and found a scaling behavior characterized by specific exponents.