Enhancement of gamma oscillations in E/I neural networks by increase of difference between external inputs

Abstract

<p style='text-indent:20px;'>Experimental observations suggest that gamma oscillations are enhanced by the increase of the difference between the components of external stimuli. To explain these experimental observations, we firstly construct a small excitatory/inhibitory (E/I) neural network of IAF neurons with external current input to E-neuron population differing from that to I-neuron population. Simulation results show that the greater the difference between the external inputs to excitatory and inhibitory neurons, the stronger gamma oscillations in the small E/I neural network. Furthermore, we construct a large-scale complicated neural network with multi-layer columns to explore gamma oscillations regulated by external stimuli which are simulated by using a novel CUDA-based algorithm. It is further found that gamma oscillations can be caused and enhanced by the difference between the external inputs in a large-scale neural network with a complicated structure. These results are consistent with the existing experimental findings well.</p>