Complexity and irregularity analysis of the output data of a cortical network

Depending on the complex interconnection of billions of neurons forming cortical network excitation times and the emergence of action potentials or spike trains becomes complex and irregular. The effect of various parameters such as synaptic connections, conductivity and voltage dependent channels on the output of the network has become of research issues. In this study, based on Hodgkin-Huxley neuron model an artificial cortical network that simulates a local region of cortex was designed and the effect of probabilistic values of network parameters used in this model on irregularity and complexity of the spike trains at the neurons´ output were investigated. Approximation Entropy, Spectral Entropy and Magnitude Squared Coherence methods were used for irregularity analysis.