This paper investigates the neural dynamics and gamma oscillation on a complex network with excitatory and inhibitory neurons (E-I network), as such network is ubiquitous in the brain. The system consists of a small-world network of neurons, which are emulated by Izhikevich model. Moreover, mixed Regular Spiking (RS) and Chattering (CH) neurons are considered to imitate excitatory neurons, and Fast Spiking (FS) neurons are used to mimic inhibitory neurons. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our model. Experiments visually demonstrate that the gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the Chattering(CH) excitatory neurons can make the system easier to synchronize.

Spiking Neural Networks(SNN) stands out as a promising solution to perform complex computations or solve pattern recognition tasks, which is based on cerebral cortical dynamics of neuroscience. However, it is challenging for SNN to accurately capture the biological properties, since most SNN algorithms depend on the different variants of Integrate-and-Fire (IF) neuron model, which produces less biophysical properties of neural networks. Learning the mathematical foundations on how mammalian neocortex mechanism is performing in information processing and artificial intelligence is particularly important. This paper investigates the neural dynamics and gamma oscillations in a complex network with balanced excitatory and inhibitory neurons (E-I network), as such networks are ubiquitous in the brain. The network consisting of hybrid regular spiking (RS) and chattering (CH) excitatory neurons and fast spiking (FS) inhibitory neurons emulated by the Izhikevich model, is designed to simulate the cortical regions of the human brain. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our method. Experiments visually demonstrate that gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the CH excitatory neurons can make the system easier to synchronize. These findings shed some light on further enhancements of the human brain and facilitate the development of artificial intelligence.