Emergence of neural persistence: Insights from computational modelling

The persistent neural activity at a global scale, either stationary or oscillatory, can be explained by the use of the excitatory-inhibitory neural network models. This state of the network, as can be inferred, is crucial for the information processing and the memorizing ability of the brain. Though the goal for persistence to exist is apparent; from where the network achieves its ability to show a rich variety of the persistent dynamical states is unclear. The following study investigates the possible reasons for the persistence of neuronal networks in two parts; numerically and analytically. Presently, it shows that the action of the inhibitory components, among other favourable factors, plays a key role in starting and stabilizing neural activity. The results strongly support previous research conducted with both simpler and more specialized neural network models, as well as neurophysiological experiments.