Neural Network Synchronization of the Morris-Lecar Neuron Model Coupled with Short-Term Plasticity (STP)

Abstract

This study used the Morris-Lecar (ML) neuron model coupled with Short-Term Plasticity (STP) to simulate neuronal connectivity and synaptic patterns. We analyze this neural network synchronization activity, examined the post-synaptic conductance patterns in the modelled neural network, investigated the dynamics of the neural network membrane potentials in the synchronous state, and analyze the Short-Term Plasticity (STP) synaptic transmission patterns by varying the inter-neuron connection probability for both inhibitory (pi) and excitatory (pe). This computational-based study was executed using Brian2 Simulator. The results revealed that the higher the connection probability, the more connections and synapses are formed. The greater value of pe, the more synchronous the neural network activity. In contrast, the higher value of pi, the less synchronous the neural network activity. A synchronous neural network implies that the spikes occur coincidentally, where coincidental spikes lead to easily detectable membrane potentials and postsynaptic conductance. Furthermore, spikes affect the release of neurotransmitters, thereby affecting synaptic transmission patterns. We further determined the frequency of this neural network synchronization.