Impact of the Excitatory-Inhibitory Neurons Ratio on Scale-Free Dynamics in a Leaky Integrate-and-Fire Model
The relationship between ratios of excitatory to inhibitory neurons and the brains 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 ratios on neural avalanches and phase transition diagrams, considering a range of control parameters 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 control parameters. In particular, a certain ratio leads to a significantly extended dynamic range compared to others and increases the probability of the system being in the critical regime. To address differences between various ratios, we utilized the Kuramoto order parameter and conducted a finite-size scaling analysis to determine the critical exponents associated with phase transitions. In order to characterize the criticality, we examined the distribution of neuronal avalanches at the critical point and the scaling behavior characterized by specific exponents.