Attaining and maintaining criticality in a neuronal network model

We propose a cellular automaton model for neuronal networks that combines short-term synaptic plasticity with long-term metaplasticity. We investigate how these two mechanisms contribute to attaining and maintaining operation at the critical point. We find that short-term plasticity, represented in the model by synaptic depression and synaptic recovery, is sufficient to allow the system to attain the critical state, if the level of plasticity is properly chosen. However, it is not sufficient to maintain the criticality if the system is perturbed. But the long time scale change in the short-term plasticity, a change in the way synaptic efficacy is modified, allows the system to recover from perturbation. Working together, these two time scales of plasticity could help the system to attain and maintain criticality, leading to a self-organized critical state.